diff --git a/CMakeLists.txt b/CMakeLists.txt index 036a5faf24f24a50361e16b5810bfc7051f07118..bd113a9ec8a9e574d046535b185d22d2644a1387 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -19,36 +19,6 @@ set(PADDLE_BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR}) include(system) -if(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK) - cmake_minimum_required(VERSION 3.10) - # TODO(TJ): make as function check_default - if(NOT DEFINED ARM_TARGET_OS) - set(ARM_TARGET_OS "android" CACHE STRING "Choose ARM Target OS") - endif() - set(ARM_TARGET_OS_LIST "android" "armlinux") # TODO: "ios" - set_property(CACHE ARM_TARGET_OS PROPERTY STRINGS ${ARM_TARGET_OS_LIST}) - if (NOT ARM_TARGET_OS IN_LIST ARM_TARGET_OS_LIST) - message(FATAL_ERROR "ARM_TARGET_OS must be in one of ${ARM_TARGET_OS_LIST}") - endif() - - if(NOT DEFINED ARM_TARGET_ARCH_ABI) - set(ARM_TARGET_ARCH_ABI "arm64-v8a" CACHE STRING "Choose ARM Target ARCH ABI") - endif() - set(ARM_TARGET_ARCH_ABI_LIST "arm64-v8a" "armeabi-v7a" "armeabi-v7a-softfp" "armeabi-v7a-hf") - set_property(CACHE ARM_TARGET_ARCH_ABI PROPERTY STRINGS ${ARM_TARGET_ARCH_ABI_LIST}) - if (NOT ARM_TARGET_ARCH_ABI IN_LIST ARM_TARGET_ARCH_ABI_LIST) - message(FATAL_ERROR "ARM_TARGET_ARCH_ABI must be in one of ${ARM_TARGET_ARCH_ABI_LIST}") - endif() - - if(NOT DEFINED TARGET_ARCH_ABI) - set(ARCH_ABI "arm64-v8a" CACHE STRING "Choose android platform") - endif() - - include(cross_compiling/host) - include(cross_compiling/armlinux) - include(cross_compiling/android) -endif() - project(paddle CXX C) message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: " "${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}") @@ -71,9 +41,7 @@ if(WIN32) set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${PADDLE_LINK_FLAGS}") endif(WIN32) -if(NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK) - find_package(CUDA QUIET) -endif() +find_package(CUDA QUIET) find_package(Git REQUIRED) find_package(Threads REQUIRED) @@ -111,79 +79,19 @@ option(PY_VERSION "Compile PaddlePaddle with python3 support" ${PY_VER option(WITH_FAST_MATH "Make use of fast math library, might affect the precision to some extent" ON) option(WITH_DGC "Use DGC(Deep Gradient Compression) or not" ON) -if(ANDROID OR IOS OR ARMLINUX) - set(WITH_GPU OFF CACHE STRING - "Disable GPU when cross-compiling for Android and iOS" FORCE) - set(WITH_DSO OFF CACHE STRING - "Disable DSO when cross-compiling for Android and iOS" FORCE) - set(WITH_AVX OFF CACHE STRING - "Disable AVX when cross-compiling for Android and iOS" FORCE) - set(WITH_PYTHON OFF CACHE STRING - "Disable PYTHON when cross-compiling for Android and iOS" FORCE) - set(WITH_RDMA OFF CACHE STRING - "Disable RDMA when cross-compiling for Android and iOS" FORCE) - set(WITH_MKL OFF CACHE STRING - "Disable MKL when cross-compiling for Android and iOS" FORCE) - - if(NOT CMAKE_BUILD_TYPE) - set(CMAKE_BUILD_TYPE "Release" CACHE STRING - "Default use Release in android" FORCE) - endif() - if(NOT THIRD_PARTY_BUILD_TYPE) - set(THIRD_PARTY_BUILD_TYPE "MinSizeRel" CACHE STRING - "Default use MinSizeRel in android" FORCE) - endif() +# PY_VERSION +if(NOT PY_VERSION) + set(PY_VERSION 2.7) endif() - -# for lite, both server and mobile framework. -option(WITH_LITE "Enable lite framework" OFF) -option(LITE_WITH_CUDA "Enable CUDA in lite mode" OFF) -option(LITE_WITH_X86 "Enable X86 in lite mode" ON) -option(LITE_WITH_ARM "Enable ARM in lite mode" OFF) -option(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK "Enable light-weight framework" OFF) -option(LITE_WITH_PROFILE "Enable profile mode in lite framework" OFF) - - -set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING - "A path setting third party libraries download & build directories.") +set(PYBIND11_PYTHON_VERSION ${PY_VERSION}) # CMAKE_BUILD_TYPE if(NOT CMAKE_BUILD_TYPE) set(CMAKE_BUILD_TYPE "RelWithDebInfo" CACHE STRING - "Choose the type of build, options are: Debug Release RelWithDebInfo MinSizeRel" - FORCE) -endif() - -include_directories("${PADDLE_SOURCE_DIR}") - -# for mobile -if (WITH_LITE AND LITE_WITH_LIGHT_WEIGHT_FRAMEWORK) - message(STATUS "Building the mobile framework") - # include the necessary thirdparty dependencies - include(external/gflags) # download, build, install gflags - include(external/glog) # download, build, install glog - include(external/gtest) # download, build, install gtest - #include(external/zlib) # download, build, install gtest - include(external/protobuf) # download, build, install protobuf - include(external/eigen) # download eigen3 - - include(generic) # simplify cmake module - include(configure) # add paddle env configuration - - add_definitions(-std=c++11) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") - add_subdirectory(paddle) - - return() + "Choose the type of build, options are: Debug Release RelWithDebInfo MinSizeRel" + FORCE) endif() - -# PY_VERSION -if(NOT PY_VERSION) - set(PY_VERSION 2.7) -endif() -set(PYBIND11_PYTHON_VERSION ${PY_VERSION}) - if (APPLE) set(WITH_MKL OFF CACHE STRING "Disable MKL for building on mac" FORCE) @@ -194,12 +102,16 @@ if (WIN32) "Disable DISTRIBUTE when compiling for Windows" FORCE) endif() +set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING + "A path setting third party libraries download & build directories.") + set(FLUID_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_install_dir" CACHE STRING "A path setting fluid shared and static libraries") set(FLUID_INFERENCE_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_inference_install_dir" CACHE STRING "A path setting fluid inference shared and static libraries") +set(THIRD_PARTY_BUILD_TYPE Release) set(WITH_MKLML ${WITH_MKL}) if (NOT DEFINED WITH_MKLDNN) @@ -273,6 +185,7 @@ if(WITH_BRPC_RDMA) endif() endif() + include(external/threadpool) include(flags) # set paddle compile flags include(cudnn) # set cudnn libraries, must before configure @@ -321,6 +234,7 @@ include(coveralls) # set code coverage include(inference_lib) # add paddle fluid inference libraries +include_directories("${PADDLE_SOURCE_DIR}") if(WITH_AMD_GPU) find_package(HIP) diff --git a/Dockerfile b/Dockerfile index c248ac119caa1f493e4866b02551eb900d3bf391..0247d1d19ce6365f12defbc3a851fa761ce96084 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,7 +1,6 @@ # A image for building paddle binaries # Use cuda devel base image for both cpu and gpu environment # When you modify it, please be aware of cudnn-runtime version -# and libcudnn.so.x in paddle/scripts/docker/build.sh FROM nvidia/cuda:8.0-cudnn7-devel-ubuntu16.04 MAINTAINER PaddlePaddle Authors @@ -76,7 +75,7 @@ RUN curl -s -q https://glide.sh/get | sh # 2. Manually add ~IPluginFactory() in IPluginFactory class of NvInfer.h, otherwise, it couldn't work in paddle. # See https://github.com/PaddlePaddle/Paddle/issues/10129 for details. -RUN wget -q https://paddlepaddledeps.cdn.bcebos.com/TensorRT-4.0.1.6-ubuntu14.04.x86_64-gnu.cuda.8.0.cudnn7.0.tar.gz --no-check-certificate && \ +RUN wget -q https://paddlepaddledeps.bj.bcebos.com/TensorRT-4.0.1.6-ubuntu14.04.x86_64-gnu.cuda.8.0.cudnn7.0.tar.gz --no-check-certificate && \ tar -zxf TensorRT-4.0.1.6-ubuntu14.04.x86_64-gnu.cuda.8.0.cudnn7.0.tar.gz -C /usr/local && \ cp -rf /usr/local/TensorRT/include /usr && \ cp -rf /usr/local/TensorRT/lib /usr @@ -93,17 +92,17 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8 # specify sphinx version as 1.5.6 and remove -U option for [pip install -U # sphinx-rtd-theme] since -U option will cause sphinx being updated to newest # version(1.7.1 for now), which causes building documentation failed. -RUN pip3 --no-cache-dir install -U wheel && \ +RUN pip3 --no-cache-dir install -U wheel py-cpuinfo==5.0.0 && \ pip3 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \ pip3 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \ - pip3.6 --no-cache-dir install -U wheel && \ + pip3.6 --no-cache-dir install -U wheel py-cpuinfo==5.0.0 && \ pip3.6 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \ pip3.6 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \ - pip3.7 --no-cache-dir install -U wheel && \ + pip3.7 --no-cache-dir install -U wheel py-cpuinfo==5.0.0 && \ pip3.7 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \ pip3.7 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \ easy_install -U pip && \ - pip --no-cache-dir install -U pip setuptools wheel && \ + pip --no-cache-dir install -U pip setuptools wheel py-cpuinfo==5.0.0 && \ pip --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \ pip --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark diff --git a/README.md b/README.md index faf8c8ee27b5810dde0811b8a8ee4d448aa1b6eb..fea320db978a1f14abc7caf941f033f8742fdcef 100644 --- a/README.md +++ b/README.md @@ -98,9 +98,11 @@ We provide [English](http://www.paddlepaddle.org/documentation/docs/en/1.4/begin We appreciate your contributions! -## Ask Questions +## Communication -You are welcome to submit questions and bug reports as [Github Issues](https://github.com/PaddlePaddle/Paddle/issues). +- [Github Issues](https://github.com/PaddlePaddle/Paddle/issues): bug reports, feature requests, install issues, usage issues, etc. +- QQ discussion group: 432676488 (PaddlePaddle). +- [Forums](http://ai.baidu.com/forum/topic/list/168?pageNo=1): discuss implementations, research, etc. ## Copyright and License PaddlePaddle is provided under the [Apache-2.0 license](LICENSE). diff --git a/README_cn.md b/README_cn.md index 17f61c70aacac7962bff2636c591f3459bace9b4..6b224ee8c51be6a87899e46427cbca053cb1e568 100644 --- a/README_cn.md +++ b/README_cn.md @@ -80,9 +80,11 @@ pip install paddlepaddle-gpu==1.4.1.post85 欢迎您的贡献! -## 答疑 +## 交流与反馈 -欢迎您将问题和bug报告以[Github Issues](https://github.com/PaddlePaddle/Paddle/issues)的形式提交 +- 欢迎您通过[Github Issues](https://github.com/PaddlePaddle/Paddle/issues)来提交问题、报告与建议 +- QQ群: 432676488 (PaddlePaddle) +- [论坛](http://ai.baidu.com/forum/topic/list/168): 欢迎大家在PaddlePaddle论坛分享在使用PaddlePaddle中遇到的问题和经验, 营造良好的论坛氛围 ## 版权和许可证 PaddlePaddle由[Apache-2.0 license](LICENSE)提供 diff --git a/cmake/anakin_subgraph.cmake b/cmake/anakin_subgraph.cmake index b5437e776d31e4d4fec5a79bf505202d192cd5ca..eb7bce9f3b7a98cb5244ea8d1ce595d5d374cda0 100644 --- a/cmake/anakin_subgraph.cmake +++ b/cmake/anakin_subgraph.cmake @@ -1,7 +1,3 @@ -if(NOT WITH_GPU) - return() -endif() - set(ANAKIN_ROOT "/usr" CACHE PATH "ANAKIN ROOT") find_path(ANAKIN_INCLUDE_DIR anakin_config.h PATHS ${ANAKIN_ROOT} ${ANAKIN_ROOT}/include @@ -16,9 +12,7 @@ find_library(ANAKIN_LIBRARY NAMES libanakin_saber_common.so libanakin.so DOC "Path to ANAKIN library.") if(ANAKIN_INCLUDE_DIR AND ANAKIN_LIBRARY) - if(WITH_DSO) set(ANAKIN_FOUND ON) - endif(WITH_DSO) else() set(ANAKIN_FOUND OFF) endif() @@ -31,3 +25,8 @@ if(ANAKIN_FOUND) link_directories(${ANAKIN_ROOT}) add_definitions(-DPADDLE_WITH_ANAKIN) endif() + +if(ANAKIN_FOUND AND WITH_GPU AND WITH_DSO) + message(STATUS "Compile with anakin subgraph.") + set(ANAKIN_SUBGRAPH ON) +endif() diff --git a/cmake/configure.cmake b/cmake/configure.cmake index 385a9572f58d520e6c0905261f9be721e85749a2..279f1eba3f567f0bfa292fad7ef7996b319cf92b 100644 --- a/cmake/configure.cmake +++ b/cmake/configure.cmake @@ -30,6 +30,7 @@ endif(NOT WITH_PROFILER) if(WITH_AVX AND AVX_FOUND) set(SIMD_FLAG ${AVX_FLAG}) + add_definitions(-DPADDLE_WITH_AVX) elseif(SSE3_FOUND) set(SIMD_FLAG ${SSE3_FLAG}) endif() @@ -157,29 +158,3 @@ endif(WITH_BRPC_RDMA) if(ON_INFER) add_definitions(-DPADDLE_ON_INFERENCE) endif(ON_INFER) - -if(WITH_WBAES) - add_definitions(-DPADDLE_WITH_WBAES) -endif(WITH_WBAES) - -# for lite -# TODO(Superjomn) not work fine with the option -if (LITE_WITH_CUDA) -add_definitions("-DLITE_WITH_CUDA") -endif() - -if (LITE_WITH_X86) - add_definitions("-DLITE_WITH_X86") -endif() - -if (LITE_WITH_ARM) - add_definitions("-DLITE_WITH_ARM") -endif() - -if (LITE_WITH_PROFILE) - add_definitions("-DLITE_WITH_PROFILE") -endif() - -if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK) - add_definitions("-DLITE_WITH_LIGHT_WEIGHT_FRAMEWORK") -endif() diff --git a/cmake/cuda.cmake b/cmake/cuda.cmake index 735846db1db04e3884d72ec62d911d9a0efec147..b9c72c046e747b8a9937e5c95b32656eb3e9d2cc 100644 --- a/cmake/cuda.cmake +++ b/cmake/cuda.cmake @@ -141,12 +141,10 @@ endfunction() message(STATUS "CUDA detected: " ${CUDA_VERSION}) if (${CUDA_VERSION} LESS 7.0) set(paddle_known_gpu_archs ${paddle_known_gpu_archs}) - add_definitions("-DPADDLE_CUDA_BINVER=\"60\"") elseif (${CUDA_VERSION} LESS 8.0) # CUDA 7.x set(paddle_known_gpu_archs ${paddle_known_gpu_archs7}) list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED") list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__") - add_definitions("-DPADDLE_CUDA_BINVER=\"70\"") elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x set(paddle_known_gpu_archs ${paddle_known_gpu_archs8}) list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED") @@ -154,18 +152,16 @@ elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x # CUDA 8 may complain that sm_20 is no longer supported. Suppress the # warning for now. list(APPEND CUDA_NVCC_FLAGS "-Wno-deprecated-gpu-targets") - add_definitions("-DPADDLE_CUDA_BINVER=\"80\"") elseif (${CUDA_VERSION} LESS 10.0) # CUDA 9.x set(paddle_known_gpu_archs ${paddle_known_gpu_archs9}) list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED") list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__") - add_definitions("-DPADDLE_CUDA_BINVER=\"90\"") elseif (${CUDA_VERSION} LESS 11.0) # CUDA 10.x set(paddle_known_gpu_archs ${paddle_known_gpu_archs10}) list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED") list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__") - add_definitions("-DPADDLE_CUDA_BINVER=\"100\"") endif() +add_definitions("-DPADDLE_CUDA_BINVER=\"${CUDA_VERSION_MAJOR}${CUDA_VERSION_MINOR}\"") include_directories(${CUDA_INCLUDE_DIRS}) if(NOT WITH_DSO) diff --git a/cmake/cudnn.cmake b/cmake/cudnn.cmake index fff1980637d029b8a392c166734d3c3b84fed867..98466d44fc0dd91ef0cc8e8eac2660c42a19267c 100644 --- a/cmake/cudnn.cmake +++ b/cmake/cudnn.cmake @@ -96,7 +96,7 @@ if(CUDNN_FOUND) endif() message(STATUS "Current cuDNN header is ${CUDNN_INCLUDE_DIR}/cudnn.h. " - "Current cuDNN version is v${CUDNN_MAJOR_VERSION}. ") + "Current cuDNN version is v${CUDNN_MAJOR_VERSION}.${CUDNN_MINOR_VERSION}. ") endif() endif() diff --git a/cmake/external/dgc.cmake b/cmake/external/dgc.cmake index a58b8c68d7716a901db1907af64c4a344a24cfc6..05e63bfe3fee58085d49736c09723953e60cfb85 100644 --- a/cmake/external/dgc.cmake +++ b/cmake/external/dgc.cmake @@ -38,5 +38,3 @@ ADD_LIBRARY(dgc STATIC IMPORTED GLOBAL) SET_PROPERTY(TARGET dgc PROPERTY IMPORTED_LOCATION ${DGC_LIBRARIES}) ADD_DEPENDENCIES(dgc extern_dgc) -LIST(APPEND external_project_dependencies dgc) - diff --git a/cmake/external/eigen.cmake b/cmake/external/eigen.cmake index 72441160f89d2c188d35fc6b08b5f0b6d746a1ad..dfe81d8f9bf2e25900b2548c707ea778e6aacf09 100644 --- a/cmake/external/eigen.cmake +++ b/cmake/external/eigen.cmake @@ -12,6 +12,13 @@ if(NOT WITH_FAST_MATH) add_definitions(-DEIGEN_FAST_MATH=0) endif() +if(WIN32) + set(EIGEN_GIT_REPOSITORY https://github.com/wopeizl/eigen-git-mirror) + set(EIGEN_GIT_TAG support_cuda9_win) +else() + set(EIGEN_GIT_REPOSITORY https://github.com/eigenteam/eigen-git-mirror) + set(EIGEN_GIT_TAG 917060c364181f33a735dc023818d5a54f60e54c) +endif() if(WITH_AMD_GPU) ExternalProject_Add( extern_eigen3 @@ -29,10 +36,10 @@ else() ExternalProject_Add( extern_eigen3 ${EXTERNAL_PROJECT_LOG_ARGS} - GIT_REPOSITORY "https://github.com/eigenteam/eigen-git-mirror" + GIT_REPOSITORY "${EIGEN_GIT_REPOSITORY}" # eigen on cuda9.1 missing header of math_funtions.hpp # https://stackoverflow.com/questions/43113508/math-functions-hpp-not-found-when-using-cuda-with-eigen - GIT_TAG 917060c364181f33a735dc023818d5a54f60e54c + GIT_TAG ${EIGEN_GIT_TAG} PREFIX ${EIGEN_SOURCE_DIR} DOWNLOAD_NAME "eigen" UPDATE_COMMAND "" diff --git a/cmake/external/gflags.cmake b/cmake/external/gflags.cmake index 42ce7c644f3e8ee51bb5fbce4391b9423ee22cf8..343b754478834a43e2403a12836e0c8de797a56b 100644 --- a/cmake/external/gflags.cmake +++ b/cmake/external/gflags.cmake @@ -18,31 +18,13 @@ SET(GFLAGS_SOURCES_DIR ${THIRD_PARTY_PATH}/gflags) SET(GFLAGS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/gflags) SET(GFLAGS_INCLUDE_DIR "${GFLAGS_INSTALL_DIR}/include" CACHE PATH "gflags include directory." FORCE) IF(WIN32) - set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE) + set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/gflags_static.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE) ELSE(WIN32) set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.a" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE) ENDIF(WIN32) INCLUDE_DIRECTORIES(${GFLAGS_INCLUDE_DIR}) -SET(OPTIONAL_ARGS "-DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}" - "-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}" - "-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}" - "-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}" - "-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}" - "-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}" - "-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}" - "-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}") - -if(ANDROID) - SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} - "-DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME}" - "-DCMAKE_SYSTEM_VERSION=${CMAKE_SYSTEM_VERSION}" - "-DCMAKE_ANDROID_ARCH_ABI=${CMAKE_ANDROID_ARCH_ABI}" - "-DCMAKE_ANDROID_NDK=${CMAKE_ANDROID_NDK}" - "-DCMAKE_ANDROID_STL_TYPE=${CMAKE_ANDROID_STL_TYPE}") -endif() - ExternalProject_Add( extern_gflags ${EXTERNAL_PROJECT_LOG_ARGS} @@ -50,24 +32,24 @@ ExternalProject_Add( GIT_TAG 77592648e3f3be87d6c7123eb81cbad75f9aef5a PREFIX ${GFLAGS_SOURCES_DIR} UPDATE_COMMAND "" - CMAKE_ARGS -DBUILD_STATIC_LIBS=ON + CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} + -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} + -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE} + -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG} + -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} + -DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG} + -DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE} + -DBUILD_STATIC_LIBS=ON -DCMAKE_INSTALL_PREFIX=${GFLAGS_INSTALL_DIR} -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DBUILD_TESTING=OFF -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE} - ${OPTIONAL_ARGS} ${EXTERNAL_OPTIONAL_ARGS} CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GFLAGS_INSTALL_DIR} -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE} ) -IF(WIN32) - IF(NOT EXISTS "${GFLAGS_INSTALL_DIR}/lib/libgflags.lib") - add_custom_command(TARGET extern_gflags POST_BUILD - COMMAND cmake -E copy ${GFLAGS_INSTALL_DIR}/lib/gflags_static.lib ${GFLAGS_INSTALL_DIR}/lib/libgflags.lib - ) - ENDIF() -ENDIF(WIN32) ADD_LIBRARY(gflags STATIC IMPORTED GLOBAL) SET_PROPERTY(TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARIES}) ADD_DEPENDENCIES(gflags extern_gflags) diff --git a/cmake/external/glog.cmake b/cmake/external/glog.cmake index 9ac9b8326431addb503acc10d3188a5f8f4e48a5..ac6294048cf7198651de292f24f97c522a5009e0 100644 --- a/cmake/external/glog.cmake +++ b/cmake/external/glog.cmake @@ -19,7 +19,7 @@ SET(GLOG_INSTALL_DIR ${THIRD_PARTY_PATH}/install/glog) SET(GLOG_INCLUDE_DIR "${GLOG_INSTALL_DIR}/include" CACHE PATH "glog include directory." FORCE) IF(WIN32) - SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.lib" CACHE FILEPATH "glog library." FORCE) + SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/glog.lib" CACHE FILEPATH "glog library." FORCE) SET(GLOG_CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4267 /wd4530") ELSE(WIN32) SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.a" CACHE FILEPATH "glog library." FORCE) @@ -31,24 +31,6 @@ INCLUDE_DIRECTORIES(${GLOG_INCLUDE_DIR}) SET(GLOG_REPOSITORY "https://github.com/google/glog.git") SET(GLOG_TAG "v0.3.5") -SET(OPTIONAL_ARGS "-DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}" - "-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}" - "-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}" - "-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}" - "-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}" - "-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}" - "-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}" - "-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}") - -if(ANDROID) - SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} - "-DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME}" - "-DCMAKE_SYSTEM_VERSION=${CMAKE_SYSTEM_VERSION}" - "-DCMAKE_ANDROID_ARCH_ABI=${CMAKE_ANDROID_ARCH_ABI}" - "-DCMAKE_ANDROID_NDK=${CMAKE_ANDROID_NDK}" - "-DCMAKE_ANDROID_STL_TYPE=${CMAKE_ANDROID_STL_TYPE}") -endif() - ExternalProject_Add( extern_glog ${EXTERNAL_PROJECT_LOG_ARGS} @@ -57,7 +39,14 @@ ExternalProject_Add( GIT_TAG ${GLOG_TAG} PREFIX ${GLOG_SOURCES_DIR} UPDATE_COMMAND "" - CMAKE_ARGS ${OPTIONAL_ARGS} + CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} + -DCMAKE_CXX_FLAGS=${GLOG_CMAKE_CXX_FLAGS} + -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE} + -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG} + -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} + -DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG} + -DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE} -DCMAKE_INSTALL_PREFIX=${GLOG_INSTALL_DIR} -DCMAKE_INSTALL_LIBDIR=${GLOG_INSTALL_DIR}/lib -DCMAKE_POSITION_INDEPENDENT_CODE=ON @@ -71,13 +60,6 @@ ExternalProject_Add( -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE} ) -IF(WIN32) - IF(NOT EXISTS "${GLOG_INSTALL_DIR}/lib/libglog.lib") - add_custom_command(TARGET extern_glog POST_BUILD - COMMAND cmake -E copy ${GLOG_INSTALL_DIR}/lib/glog.lib ${GLOG_INSTALL_DIR}/lib/libglog.lib - ) - ENDIF() -ENDIF(WIN32) ADD_LIBRARY(glog STATIC IMPORTED GLOBAL) SET_PROPERTY(TARGET glog PROPERTY IMPORTED_LOCATION ${GLOG_LIBRARIES}) diff --git a/cmake/external/gtest.cmake b/cmake/external/gtest.cmake index de44719803fc4f130d536c2354fa492a57e3e69a..e459526583bd5ee3c89807657f3c30376e57d971 100644 --- a/cmake/external/gtest.cmake +++ b/cmake/external/gtest.cmake @@ -43,24 +43,6 @@ IF(WITH_TESTING OR (WITH_DISTRIBUTE AND NOT WITH_GRPC)) SET(GTEST_DEPENDS ${MKLML_PROJECT}) ENDIF() - SET(OPTIONAL_ARGS "-DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}" - "-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}" - "-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}" - "-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}" - "-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}" - "-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}" - "-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}" - "-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}") - - if(ANDROID) - SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} - "-DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME}" - "-DCMAKE_SYSTEM_VERSION=${CMAKE_SYSTEM_VERSION}" - "-DCMAKE_ANDROID_ARCH_ABI=${CMAKE_ANDROID_ARCH_ABI}" - "-DCMAKE_ANDROID_NDK=${CMAKE_ANDROID_NDK}" - "-DCMAKE_ANDROID_STL_TYPE=${CMAKE_ANDROID_STL_TYPE}") - endif() - ExternalProject_Add( extern_gtest ${EXTERNAL_PROJECT_LOG_ARGS} @@ -69,7 +51,14 @@ IF(WITH_TESTING OR (WITH_DISTRIBUTE AND NOT WITH_GRPC)) GIT_TAG "release-1.8.0" PREFIX ${GTEST_SOURCES_DIR} UPDATE_COMMAND "" - CMAKE_ARGS ${OPTIONAL_ARGS} + CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} + -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} + -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE} + -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG} + -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} + -DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG} + -DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE} -DCMAKE_INSTALL_PREFIX=${GTEST_INSTALL_DIR} -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DBUILD_GMOCK=ON diff --git a/cmake/external/mklml.cmake b/cmake/external/mklml.cmake index 142fce816de4f06aa0a36b91e3e4ecb962a8dc2a..066811296e1e99f6d42348ba5c526d9243c7e62f 100644 --- a/cmake/external/mklml.cmake +++ b/cmake/external/mklml.cmake @@ -38,6 +38,7 @@ IF(WIN32) SET(MKLML_LIB ${MKLML_LIB_DIR}/mklml.lib) SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.lib) SET(MKLML_SHARED_LIB ${MKLML_LIB_DIR}/mklml.dll) + SET(MKLML_SHARED_LIB_DEPS ${MKLML_LIB_DIR}/msvcr120.dll) SET(MKLML_SHARED_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.dll) ELSE() #TODO(intel-huying): diff --git a/cmake/external/ngraph.cmake b/cmake/external/ngraph.cmake index d00195b08d220ef34f042b26d8523db856f0e431..cdcbdd46a8d55cc75706de7bc415478f4fe4f256 100644 --- a/cmake/external/ngraph.cmake +++ b/cmake/external/ngraph.cmake @@ -37,7 +37,7 @@ INCLUDE(GNUInstallDirs) INCLUDE(ExternalProject) SET(NGRAPH_PROJECT "extern_ngraph") -SET(NGRAPH_GIT_TAG "127e0dedfaac8c6f2b148cc03bf5f67ac5fbe6fe") +SET(NGRAPH_GIT_TAG "4ec94acc11084a5d53418f565529310fa584899a") SET(NGRAPH_SOURCES_DIR ${THIRD_PARTY_PATH}/ngraph) SET(NGRAPH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/ngraph) SET(NGRAPH_INC_DIR ${NGRAPH_INSTALL_DIR}/include) diff --git a/cmake/external/protobuf.cmake b/cmake/external/protobuf.cmake index 41cd1ebaf33a6ec7c61ee8c965eaa0bccbb618b8..09eb437aede4364f8aa285d5296f21cd8460fca1 100644 --- a/cmake/external/protobuf.cmake +++ b/cmake/external/protobuf.cmake @@ -142,6 +142,7 @@ IF (WIN32) ENDIF(WIN32) if (NOT "${PROTOBUF_ROOT}" STREQUAL "") + find_path(PROTOBUF_INCLUDE_DIR google/protobuf/message.h PATHS ${PROTOBUF_ROOT}/include NO_DEFAULT_PATH) find_library(PROTOBUF_LIBRARY protobuf libprotobuf.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH) find_library(PROTOBUF_LITE_LIBRARY protobuf-lite libprotobuf-lite.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH) @@ -177,28 +178,12 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST) "${PROTOBUF_INSTALL_DIR}/bin/protoc${CMAKE_EXECUTABLE_SUFFIX}" PARENT_SCOPE) - SET(PROTOBUF_REPO "https://github.com/protocolbuffers/protobuf.git") - SET(PROTOBUF_TAG "9f75c5aa851cd877fb0d93ccc31b8567a6706546") SET(OPTIONAL_CACHE_ARGS "") SET(OPTIONAL_ARGS "") - IF(BUILD_FOR_HOST) - SET(OPTIONAL_ARGS - "-DCMAKE_C_COMPILER=${HOST_C_COMPILER}" - "-DCMAKE_CXX_COMPILER=${HOST_CXX_COMPILER}" - "-Dprotobuf_WITH_ZLIB=OFF" - "-DZLIB_ROOT:FILEPATH=${ZLIB_ROOT}") - SET(OPTIONAL_CACHE_ARGS "-DZLIB_ROOT:STRING=${ZLIB_ROOT}") + SET(OPTIONAL_ARGS "-Dprotobuf_WITH_ZLIB=OFF") ELSE() - # protobuf have compile issue when use android stl c++_static - SET(PROTOBUF_REPO "https://github.com/tensor-tang/protobuf.git") - SET(PROTOBUF_TAG "mobile") - SET(OPTIONAL_ARGS "-Dprotobuf_WITH_ZLIB=OFF" - "-DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME}" - "-DCMAKE_SYSTEM_VERSION=${CMAKE_SYSTEM_VERSION}" - "-DCMAKE_ANDROID_ARCH_ABI=${CMAKE_ANDROID_ARCH_ABI}" - "-DCMAKE_ANDROID_NDK=${CMAKE_ANDROID_NDK}" - "-DCMAKE_ANDROID_STL_TYPE=${CMAKE_ANDROID_STL_TYPE}" + SET(OPTIONAL_ARGS "-DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}" "-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}" "-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}" @@ -206,18 +191,25 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST) "-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}" "-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}" "-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}" - "-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}") + "-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}" + "-Dprotobuf_WITH_ZLIB=ON" + "-DZLIB_ROOT:FILEPATH=${ZLIB_ROOT}" + ${EXTERNAL_OPTIONAL_ARGS}) + SET(OPTIONAL_CACHE_ARGS "-DZLIB_ROOT:STRING=${ZLIB_ROOT}") ENDIF() IF(WIN32) SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} "-DCMAKE_GENERATOR_PLATFORM=x64") ENDIF() + SET(PROTOBUF_REPO "https://github.com/protocolbuffers/protobuf.git") + SET(PROTOBUF_TAG "9f75c5aa851cd877fb0d93ccc31b8567a6706546") + ExternalProject_Add( ${TARGET_NAME} ${EXTERNAL_PROJECT_LOG_ARGS} PREFIX ${PROTOBUF_SOURCES_DIR} UPDATE_COMMAND "" - #DEPENDS zlib + DEPENDS zlib GIT_REPOSITORY ${PROTOBUF_REPO} GIT_TAG ${PROTOBUF_TAG} CONFIGURE_COMMAND @@ -241,13 +233,6 @@ ENDFUNCTION() SET(PROTOBUF_VERSION 3.1.0) -IF(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK) - build_protobuf(protobuf_host TRUE) - LIST(APPEND external_project_dependencies protobuf_host) - SET(PROTOBUF_PROTOC_EXECUTABLE ${protobuf_host_PROTOC_EXECUTABLE} - CACHE FILEPATH "protobuf executable." FORCE) -ENDIF() - IF(NOT PROTOBUF_FOUND) build_protobuf(extern_protobuf FALSE) @@ -260,12 +245,7 @@ IF(NOT PROTOBUF_FOUND) SET(PROTOBUF_PROTOC_LIBRARY ${extern_protobuf_PROTOC_LIBRARY} CACHE FILEPATH "protoc library." FORCE) - IF(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK) - PROMPT_PROTOBUF_LIB(protobuf_host extern_protobuf) - ELSE() - SET(PROTOBUF_PROTOC_EXECUTABLE ${extern_protobuf_PROTOC_EXECUTABLE} - CACHE FILEPATH "protobuf executable." FORCE) - PROMPT_PROTOBUF_LIB(extern_protobuf) - ENDIF() - + SET(PROTOBUF_PROTOC_EXECUTABLE ${extern_protobuf_PROTOC_EXECUTABLE} + CACHE FILEPATH "protobuf executable." FORCE) + PROMPT_PROTOBUF_LIB(extern_protobuf) ENDIF(NOT PROTOBUF_FOUND) diff --git a/cmake/external/pslib.cmake b/cmake/external/pslib.cmake index 0287e5cf2a835ed65c5fc26ff69d2653d5db217e..b7159d14c11f0ad2e9cb5c5617f6065f57849642 100644 --- a/cmake/external/pslib.cmake +++ b/cmake/external/pslib.cmake @@ -29,9 +29,9 @@ INCLUDE(ExternalProject) SET(PSLIB_PROJECT "extern_pslib") IF((NOT DEFINED PSLIB_VER) OR (NOT DEFINED PSLIB_URL)) MESSAGE(STATUS "use pre defined download url") - SET(PSLIB_VER "0.1.0" CACHE STRING "" FORCE) - SET(PSLIB_NAME "pslib" CACHE STRING "" FORCE) - SET(PSLIB_URL "https://raw.githubusercontent.com/PaddlePaddle/Fleet/release/${PSLIB_VER}/${PSLIB_NAME}.tar.gz" CACHE STRING "" FORCE) + SET(PSLIB_VER "0.1.1" CACHE STRING "" FORCE) + SET(PSLIB_NAME "pslib" CACHE STRING "" FORCE) + SET(PSLIB_URL "https://raw.githubusercontent.com/PaddlePaddle/Fleet/release/${PSLIB_VER}/ps/${PSLIB_NAME}.tar.gz" CACHE STRING "" FORCE) ENDIF() MESSAGE(STATUS "PSLIB_NAME: ${PSLIB_NAME}, PSLIB_URL: ${PSLIB_URL}") SET(PSLIB_SOURCE_DIR "${THIRD_PARTY_PATH}/pslib") diff --git a/cmake/external/snappy.cmake b/cmake/external/snappy.cmake index 1e01057aa606af78cd722d3619a710cb35817174..3fb6b49f472df48b77ca689f4ef22e6abc2902a9 100644 --- a/cmake/external/snappy.cmake +++ b/cmake/external/snappy.cmake @@ -53,12 +53,7 @@ ExternalProject_Add( -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE} ) IF(WIN32) - IF(NOT EXISTS "${SNAPPY_INSTALL_DIR}/lib/libsnappy.lib") - add_custom_command(TARGET extern_snappy POST_BUILD - COMMAND cmake -E copy ${SNAPPY_INSTALL_DIR}/lib/snappy.lib ${SNAPPY_INSTALL_DIR}/lib/libsnappy.lib - ) - ENDIF() - set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/libsnappy.lib") + set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/snappy.lib") else(WIN32) set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/libsnappy.a") endif (WIN32) diff --git a/cmake/external/warpctc.cmake b/cmake/external/warpctc.cmake index 012283c6ea7762f3932ce55d5f86c16623679e75..5fc46ae8eb8623ee6677cea7e62ce0329c57e1f2 100644 --- a/cmake/external/warpctc.cmake +++ b/cmake/external/warpctc.cmake @@ -64,12 +64,7 @@ ExternalProject_Add( -DCMAKE_INSTALL_PREFIX:PATH=${WARPCTC_INSTALL_DIR} ) IF(WIN32) - IF(NOT EXISTS "${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}") - add_custom_command(TARGET extern_warpctc POST_BUILD - COMMAND cmake -E copy ${WARPCTC_INSTALL_DIR}/bin/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX} ${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX} - ) - ENDIF() - SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}" + SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/bin/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}" CACHE FILEPATH "Warp-ctc Library" FORCE) else(WIN32) SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}" diff --git a/cmake/external/xxhash.cmake b/cmake/external/xxhash.cmake index 23b1e02108642df561948a6faa3152effb7ca932..262d47f6fd409e6bb6b5402a646c87d8a3dbb4fe 100644 --- a/cmake/external/xxhash.cmake +++ b/cmake/external/xxhash.cmake @@ -56,12 +56,7 @@ else() endif() if (WIN32) - IF(NOT EXISTS "${XXHASH_INSTALL_DIR}/lib/libxxhash.lib") - add_custom_command(TARGET extern_xxhash POST_BUILD - COMMAND cmake -E copy ${XXHASH_INSTALL_DIR}/lib/xxhash.lib ${XXHASH_INSTALL_DIR}/lib/libxxhash.lib - ) - ENDIF() - set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/libxxhash.lib") + set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/xxhash.lib") else() set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/libxxhash.a") endif () diff --git a/cmake/external/zlib.cmake b/cmake/external/zlib.cmake index 5569fefe992d10ad4820e51e677f40271d0214e7..58881ac2206d844acf56c3dd67138ca18f59eb49 100644 --- a/cmake/external/zlib.cmake +++ b/cmake/external/zlib.cmake @@ -44,12 +44,7 @@ ExternalProject_Add( -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE} ) IF(WIN32) - IF(NOT EXISTS "${ZLIB_INSTALL_DIR}/lib/libz.lib") - add_custom_command(TARGET extern_zlib POST_BUILD - COMMAND cmake -E copy ${ZLIB_INSTALL_DIR}/lib/zlibstatic.lib ${ZLIB_INSTALL_DIR}/lib/libz.lib - ) - ENDIF() - SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/libz.lib" CACHE FILEPATH "zlib library." FORCE) + SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/zlibstatic.lib" CACHE FILEPATH "zlib library." FORCE) ELSE(WIN32) SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/libz.a" CACHE FILEPATH "zlib library." FORCE) ENDIF(WIN32) diff --git a/cmake/generic.cmake b/cmake/generic.cmake index a028dcbd6be80dd94cf33c333a1bd823b9c13298..3e3a5ba66c800972d3f9eb24967851e7c3d00361 100644 --- a/cmake/generic.cmake +++ b/cmake/generic.cmake @@ -93,10 +93,7 @@ include_directories(${CMAKE_CURRENT_BINARY_DIR}) if(NOT APPLE) find_package(Threads REQUIRED) link_libraries(${CMAKE_THREAD_LIBS_INIT}) - set(CMAKE_CXX_LINK_EXECUTABLE "${CMAKE_CXX_LINK_EXECUTABLE} -pthread -ldl") - if (NOT ANDROID) - set(CMAKE_CXX_LINK_EXECUTABLE "${CMAKE_CXX_LINK_EXECUTABLE} -lrt") - endif() + set(CMAKE_CXX_LINK_EXECUTABLE "${CMAKE_CXX_LINK_EXECUTABLE} -pthread -ldl -lrt") endif(NOT APPLE) set_property(GLOBAL PROPERTY FLUID_MODULES "") @@ -366,11 +363,10 @@ function(cc_binary TARGET_NAME) target_link_libraries(${TARGET_NAME} ${os_dependency_modules}) endfunction(cc_binary) -function(cc_test TARGET_NAME) +function(cc_test_build TARGET_NAME) if(WITH_TESTING) - set(options SERIAL) set(oneValueArgs "") - set(multiValueArgs SRCS DEPS ARGS) + set(multiValueArgs SRCS DEPS) cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) add_executable(${TARGET_NAME} ${cc_test_SRCS}) if(WIN32) @@ -383,12 +379,18 @@ function(cc_test TARGET_NAME) target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} ${os_dependency_modules} paddle_gtest_main lod_tensor memory gtest gflags glog) add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog) common_link(${TARGET_NAME}) + endif() +endfunction() + +function(cc_test_run TARGET_NAME) + if(WITH_TESTING) + set(oneValueArgs "") + set(multiValueArgs COMMAND ARGS) + cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) add_test(NAME ${TARGET_NAME} - COMMAND ${TARGET_NAME} ${cc_test_ARGS} - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}) - if (${cc_test_SERIAL}) - set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1) - endif() + COMMAND ${cc_test_COMMAND} + ARGS ${cc_test_ARGS} + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}) set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cpu_deterministic=true) set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true) set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_limit_of_tmp_allocation=4294967296) # 4G @@ -396,46 +398,21 @@ function(cc_test TARGET_NAME) # No unit test should exceed 10 minutes. set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600) endif() -endfunction(cc_test) +endfunction() -# cc_test without default dependencies -function(raw_cc_test TARGET_NAME) +function(cc_test TARGET_NAME) if(WITH_TESTING) - set(options SERIAL) set(oneValueArgs "") set(multiValueArgs SRCS DEPS ARGS) cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) - add_executable(${TARGET_NAME} ${cc_test_SRCS}) - if(WIN32) - if("${cc_test_DEPS};" MATCHES "python;") - list(REMOVE_ITEM cc_test_DEPS python) - target_link_libraries(${TARGET_NAME} ${PYTHON_LIBRARIES}) - endif() - endif(WIN32) - get_property(os_dependency_modules GLOBAL PROPERTY OS_DEPENDENCY_MODULES) - target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} ${os_dependency_modules} lite_gtest_main gtest gflags glog) - add_dependencies(${TARGET_NAME} ${cc_test_DEPS} lite_gtest_main gtest gflags glog) - common_link(${TARGET_NAME}) - add_test(NAME ${TARGET_NAME} - COMMAND ${TARGET_NAME} ${cc_test_ARGS} - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}) - if (${cc_test_SERIAL}) - set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1) - endif() - # No unit test should exceed 10 minutes. - set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600) + cc_test_build(${TARGET_NAME} + SRCS ${cc_test_SRCS} + DEPS ${cc_test_DEPS}) + cc_test_run(${TARGET_NAME} + COMMAND ${TARGET_NAME} + ARGS ${cc_test_ARGS}) endif() -endfunction(raw_cc_test) - -function(_lite_cc_test args) - if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK) - message(STATUS "building lite raw test: ${args}") - raw_cc_test(${args} ${ARGN}) - else() - message(STATUS "building lite heavy test: ${args}") - cc_test(${args} ${ARGN}) - endif() -endfunction() +endfunction(cc_test) function(nv_library TARGET_NAME) if (WITH_GPU) @@ -488,7 +465,6 @@ endfunction(nv_binary) function(nv_test TARGET_NAME) if (WITH_GPU AND WITH_TESTING) - set(options SERIAL) set(oneValueArgs "") set(multiValueArgs SRCS DEPS) cmake_parse_arguments(nv_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) @@ -498,9 +474,6 @@ function(nv_test TARGET_NAME) add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog) common_link(${TARGET_NAME}) add_test(${TARGET_NAME} ${TARGET_NAME}) - if (nv_test_SERIAL) - set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1) - endif() set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cpu_deterministic=true) set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true) set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_limit_of_tmp_allocation=4294967296) # 4G @@ -743,7 +716,7 @@ function(py_proto_compile TARGET_NAME) cmake_parse_arguments(py_proto_compile "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) set(py_srcs) protobuf_generate_python(py_srcs ${py_proto_compile_SRCS}) - add_custom_target(${TARGET_NAME} ALL DEPENDS ${py_srcs}) + add_custom_target(${TARGET_NAME} ALL DEPENDS ${py_srcs} protobuf) endfunction() function(py_test TARGET_NAME) diff --git a/cmake/operators.cmake b/cmake/operators.cmake index c17e718f4279f24c85db8be1177e5b5e82b13e08..134c894392a604875780fcfc8ea93e06c9d48bdd 100644 --- a/cmake/operators.cmake +++ b/cmake/operators.cmake @@ -110,7 +110,7 @@ function(op_library TARGET) # Define operators that don't need pybind here. foreach(manual_pybind_op "compare_op" "logical_op" "nccl_op" "tensor_array_read_write_op" "tensorrt_engine_op" "conv_fusion_op" -"fusion_transpose_flatten_concat_op" "fusion_conv_inception_op" "sync_batch_norm_op" "dgc_op") +"fusion_transpose_flatten_concat_op" "fusion_conv_inception_op" "sync_batch_norm_op" "deformable_conv_op" "dgc_op") if ("${TARGET}" STREQUAL "${manual_pybind_op}") set(pybind_flag 1) endif() diff --git a/cmake/version.cmake b/cmake/version.cmake index f7b065b582c52d7e45d2260b1db304d182f9066c..dd57d4ab9969ce530f93ca1694350b1a26b5b543 100644 --- a/cmake/version.cmake +++ b/cmake/version.cmake @@ -3,8 +3,6 @@ set(PADDLE_VERSION $ENV{PADDLE_VERSION}) set(tmp_version "HEAD") set(TAG_VERSION_REGEX "[0-9]+\\.[0-9]+\\.[0-9]+(\\.(a|b|rc)\\.[0-9]+)?") set(COMMIT_VERSION_REGEX "[0-9a-f]+[0-9a-f]+[0-9a-f]+[0-9a-f]+[0-9a-f]+") -set(LATEST_PADDLE_VERSION "latest") - while ("${PADDLE_VERSION}" STREQUAL "") # Check current branch name execute_process( @@ -25,8 +23,8 @@ while ("${PADDLE_VERSION}" STREQUAL "") if (${GIT_BRANCH_NAME} MATCHES "release/${TAG_VERSION_REGEX}") # Check the tag is a correct version if (${GIT_TAG_NAME} MATCHES "${COMMIT_VERSION_REGEX}") - # if no tag was found, set PADDLE_VERSION to "latest" - set(PADDLE_VERSION "${LATEST_PADDLE_VERSION}") + # if no tag was found, set PADDLE_VERSION to 0.0.0 to represent latest + set(PADDLE_VERSION "0.0.0") elseif (${GIT_TAG_NAME} MATCHES "v${TAG_VERSION_REGEX}") string(REPLACE "v" "" PADDLE_VERSION ${GIT_TAG_NAME}) else() # otherwise, get the previous git tag name. @@ -44,19 +42,19 @@ while ("${PADDLE_VERSION}" STREQUAL "") if (${GIT_EXACT_TAG_NAME} MATCHES "v${TAG_VERSION_REGEX}") string(REPLACE "v" "" PADDLE_VERSION ${GIT_EXACT_TAG_NAME}) else() - set(PADDLE_VERSION "${LATEST_PADDLE_VERSION}") + set(PADDLE_VERSION "0.0.0") endif() else() - # otherwise, we always set PADDLE_VERSION to "latest" - set(PADDLE_VERSION "${LATEST_PADDLE_VERSION}") + # otherwise, we always set PADDLE_VERSION to 0.0.0 to represent latest + set(PADDLE_VERSION "0.0.0") endif() endif() else() - set(PADDLE_VERSION "${LATEST_PADDLE_VERSION}") + set(PADDLE_VERSION "0.0.0") message(WARNING "Cannot add paddle version from git tag") endif() else() - set(PADDLE_VERSION "${LATEST_PADDLE_VERSION}") + set(PADDLE_VERSION "0.0.0") message(WARNING "Cannot add paddle version for wrong git branch result") endif() endwhile() diff --git a/paddle/CMakeLists.txt b/paddle/CMakeLists.txt index 7eefaa12dfcab71e2a296f4270ca025fbb1b99bd..c0c04d475959de2bfd6505b6ed30d5c18cbd99da 100644 --- a/paddle/CMakeLists.txt +++ b/paddle/CMakeLists.txt @@ -1,7 +1,4 @@ -# to limit the mobile dependencies -if (NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK) - add_subdirectory(scripts) - add_subdirectory(testing) - set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests CACHE INTERNAL "python tests directory") -endif() +add_subdirectory(scripts) +add_subdirectory(testing) +set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests CACHE INTERNAL "python tests directory") add_subdirectory(fluid) diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec index fd9567dd6517e756b2c1e83ee502c92bd4a440cf..052816abbb600f91a771ab63dd3af4d50b802417 100644 --- a/paddle/fluid/API.spec +++ b/paddle/fluid/API.spec @@ -1,14 +1,14 @@ paddle.fluid.Program.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.Program.block (ArgSpec(args=['self', 'index'], varargs=None, keywords=None, defaults=None), ('document', 'af5346376065ff4cf6832a8ac0ae0945')) -paddle.fluid.Program.clone (ArgSpec(args=['self', 'for_test'], varargs=None, keywords=None, defaults=(False,)), ('document', 'ebb7765b2962bd2be041d19720e49d0f')) -paddle.fluid.Program.current_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '5e162d3bf8dd625703463d9e4be36adb')) -paddle.fluid.Program.global_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'cfb7e05a002b2e64650778cabde7301c')) -paddle.fluid.Program.list_vars (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '1c8647b14fe57c7824b1c9562394dd3c')) +paddle.fluid.Program.block (ArgSpec(args=['self', 'index'], varargs=None, keywords=None, defaults=None), ('document', '86cd9499e226be661a3d686260ee1150')) +paddle.fluid.Program.clone (ArgSpec(args=['self', 'for_test'], varargs=None, keywords=None, defaults=(False,)), ('document', '17d059efb24c81dde6166c6b0b93e9d0')) +paddle.fluid.Program.current_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'd601c7719e425e3d9cf862ea4ad194ca')) +paddle.fluid.Program.global_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'd64ea1dc96e9f674499ea3006d470aa4')) +paddle.fluid.Program.list_vars (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '32c14b0f12baae4b352200fa09b5e789')) paddle.fluid.Program.parse_from_string (ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None), ('document', 'b6a7ffb239a30bf2ce58cfaca8d8b8d5')) -paddle.fluid.Program.to_string (ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,)), ('document', 'faec17e5a04af28e3776160e34504d15')) -paddle.fluid.default_startup_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '99e5d53d92d82797093332719c9e3ccd')) -paddle.fluid.default_main_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '5430f54ab4895f9f47db6bebbaf71659')) -paddle.fluid.program_guard (ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ae5f806f082cfaeaa5194cacc253a5e4')) +paddle.fluid.Program.to_string (ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,)), ('document', '89acca639baf00f3ad08b9d827e81706')) +paddle.fluid.default_startup_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'ba609cb02e4e55e8d626723567ef1778')) +paddle.fluid.default_main_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '06a5a8f649dfb8496c1f683c909db375')) +paddle.fluid.program_guard (ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,)), ('document', '78fb5c7f70ef76bcf4a1862c3f6b8191')) paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,)), ('document', '61660461e1f44e0480ca22fa8a482c41')) paddle.fluid.cuda_places (ArgSpec(args=['device_ids'], varargs=None, keywords=None, defaults=(None,)), ('document', '7f3068b82fc427bfa04b1af953610992')) paddle.fluid.cpu_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', '8b674e9a7ac7944c27fd853b675c2cb2')) @@ -22,40 +22,41 @@ paddle.fluid.Executor.train_from_dataset (ArgSpec(args=['self', 'program', 'data paddle.fluid.global_scope (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'f65788d9ead293ada47551339df12203')) paddle.fluid.scope_guard (ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None), ('document', '6e19f92e2f185320a3a86b77e85eb3b3')) paddle.fluid.DistributeTranspiler.__init__ (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.DistributeTranspiler.get_pserver_program (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '292ab72977afbe58e6a3bde175452680')) -paddle.fluid.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '78f4949aedf317666a89ca74b3748ba8')) -paddle.fluid.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'd796fc0c8d51503b556fcf6dc15c4f0c')) -paddle.fluid.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '736330e31a7a54abccc0c7fd9119d9ff')) -paddle.fluid.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '06ce55338dfe96311ad1078235ab3bf4')) -paddle.fluid.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)), ('document', 'eda17d0f1639bc6ca215cecf87f588a4')) -paddle.fluid.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ac4114d3df16264f1946deb3a8434a6f')) +paddle.fluid.DistributeTranspiler.get_pserver_program (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', 'b1951949c6d21698290aa8ac69afee32')) +paddle.fluid.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', 'c89fc350f975ef827f5448d68af388cf')) +paddle.fluid.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', '90a40b80e0106f69262cc08b861c3e39')) +paddle.fluid.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '0e47f020304e2b824e87ff03475c17cd')) +paddle.fluid.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '418c7e8b268e9be4104f2809e654c2f7')) +paddle.fluid.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)), ('document', '2348247f684bfd5bb9466470f35be064')) +paddle.fluid.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd38c5b8b2b2e0bb19bcf1b581a80a7e4')) paddle.fluid.DistributeTranspilerConfig.__init__ paddle.fluid.ParallelExecutor.__init__ (ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id', 'scope'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 1, 0, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.ParallelExecutor.drop_local_exe_scopes (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '80d857dc626612e2b2460d0154551e95')) +paddle.fluid.ParallelExecutor.drop_local_exe_scopes (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '77c739744ea5708b80fb1b37cc89db40')) paddle.fluid.ParallelExecutor.run (ArgSpec(args=['self', 'fetch_list', 'feed', 'feed_dict', 'return_numpy'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', '33ce6ec50f8eeb05d340e6b114b026fd')) paddle.fluid.create_lod_tensor (ArgSpec(args=['data', 'recursive_seq_lens', 'place'], varargs=None, keywords=None, defaults=None), ('document', 'b82ea20e2dc5ff2372e0643169ca47ff')) paddle.fluid.create_random_int_lodtensor (ArgSpec(args=['recursive_seq_lens', 'base_shape', 'place', 'low', 'high'], varargs=None, keywords=None, defaults=None), ('document', '74dc6d23185d90a7a50fbac19f5b65fb')) paddle.fluid.DataFeedDesc.__init__ (ArgSpec(args=['self', 'proto_file'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.DataFeedDesc.desc (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '4294493e31c4bc9fc4bd48753044235f')) -paddle.fluid.DataFeedDesc.set_batch_size (ArgSpec(args=['self', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', '8d9f44601e0a99dd431f14fd9250cd21')) -paddle.fluid.DataFeedDesc.set_dense_slots (ArgSpec(args=['self', 'dense_slots_name'], varargs=None, keywords=None, defaults=None), ('document', 'eb894b464bbcd1b4bc8038398954f766')) -paddle.fluid.DataFeedDesc.set_use_slots (ArgSpec(args=['self', 'use_slots_name'], varargs=None, keywords=None, defaults=None), ('document', '415c56600ce4e198c071cad01409a690')) +paddle.fluid.DataFeedDesc.desc (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '75283b5f03ec7b6f74bfca9881a37428')) +paddle.fluid.DataFeedDesc.set_batch_size (ArgSpec(args=['self', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', '68df53d3ea0f24063bf7689e82c2b82e')) +paddle.fluid.DataFeedDesc.set_dense_slots (ArgSpec(args=['self', 'dense_slots_name'], varargs=None, keywords=None, defaults=None), ('document', 'd5a78553cd94fe64148399797055d8ad')) +paddle.fluid.DataFeedDesc.set_use_slots (ArgSpec(args=['self', 'use_slots_name'], varargs=None, keywords=None, defaults=None), ('document', '88d229ea9f892ce8d2922cf028c8bb3a')) paddle.fluid.CompiledProgram.__init__ (ArgSpec(args=['self', 'program_or_graph'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.CompiledProgram.with_data_parallel (ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from', 'places'], varargs=None, keywords=None, defaults=(None, None, None, None, None)), ('document', '0e17773521634ef798fddd7d2ea3ef96')) paddle.fluid.CompiledProgram.with_inference_optimize (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=None), ('document', '9e5b009d850191a010e859189c127fd8')) -paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.ExecutionStrategy) -> None -paddle.fluid.BuildStrategy.GradientScaleStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy.GradientScaleStrategy, arg0: int) -> None -paddle.fluid.BuildStrategy.ReduceStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy.ReduceStrategy, arg0: int) -> None -paddle.fluid.BuildStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy) -> None -paddle.fluid.io.save_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b55d6193a1d4198d45b013fc5779e1f2')) -paddle.fluid.io.save_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '3a7a99abac3e1bf898871fe609354218')) +paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core_avx.ParallelExecutor.ExecutionStrategy) -> None +paddle.fluid.BuildStrategy.GradientScaleStrategy.__init__ __init__(self: paddle.fluid.core_avx.ParallelExecutor.BuildStrategy.GradientScaleStrategy, arg0: int) -> None +paddle.fluid.BuildStrategy.ReduceStrategy.__init__ __init__(self: paddle.fluid.core_avx.ParallelExecutor.BuildStrategy.ReduceStrategy, arg0: int) -> None +paddle.fluid.BuildStrategy.__init__ __init__(self: paddle.fluid.core_avx.ParallelExecutor.BuildStrategy) -> None +paddle.fluid.gradients (ArgSpec(args=['targets', 'inputs', 'target_gradients', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'e2097e1e0ed84ae44951437bfe269a1b')) +paddle.fluid.io.save_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '869104f47e6fd21d897c3fcc426aa942')) +paddle.fluid.io.save_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '07ffd5351b30cf47172ccfd61bd0de6f')) paddle.fluid.io.save_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '9141bb5f32caf7975eb3fd88c8a1b2da')) -paddle.fluid.io.load_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '0a5308f496632ab1ec3ba1f1377e6f95')) -paddle.fluid.io.load_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '41779819cef32f2246e83aebc5a002e2')) +paddle.fluid.io.load_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '1bb9454cf09d71f190bb51550c5a3ac9')) +paddle.fluid.io.load_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '944291120d37bdb037a689d2c86d0a6e')) paddle.fluid.io.load_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '28df5bfe26ca7a077f91156abb0fe6d2')) -paddle.fluid.io.save_inference_model (ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True)), ('document', 'af82e1b5fe5764029905a191b987f63d')) -paddle.fluid.io.load_inference_model (ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '648f64d8fd81572eef34a69e533459ef')) -paddle.fluid.io.PyReader.__init__ (ArgSpec(args=['self', 'feed_list', 'capacity', 'use_double_buffer', 'iterable'], varargs=None, keywords=None, defaults=(True, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.io.save_inference_model (ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment', 'program_only'], varargs=None, keywords=None, defaults=(None, None, None, True, False)), ('document', 'fc82bfd137a9b1ab8ebd1651bd35b6e5')) +paddle.fluid.io.load_inference_model (ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '2f54d7c206b62f8c10f4f9d78c731cfd')) +paddle.fluid.io.PyReader.__init__ (ArgSpec(args=['self', 'feed_list', 'capacity', 'use_double_buffer', 'iterable', 'return_list'], varargs=None, keywords=None, defaults=(None, None, True, True, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.io.PyReader.decorate_batch_generator (ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,)), ('document', '4a072de39998ee4e0de33fcec11325a6')) paddle.fluid.io.PyReader.decorate_sample_generator (ArgSpec(args=['self', 'sample_generator', 'batch_size', 'drop_last', 'places'], varargs=None, keywords=None, defaults=(True, None)), ('document', '3db4b24d33fe4f711e303f9673dc5c6a')) paddle.fluid.io.PyReader.decorate_sample_list_generator (ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,)), ('document', '94adc0fb71c4b2ae6c3c74886c9cb898')) @@ -68,137 +69,137 @@ paddle.fluid.initializer.TruncatedNormalInitializer.__init__ (ArgSpec(args=['sel paddle.fluid.initializer.XavierInitializer.__init__ (ArgSpec(args=['self', 'uniform', 'fan_in', 'fan_out', 'seed'], varargs=None, keywords=None, defaults=(True, None, None, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.initializer.BilinearInitializer.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'd389912dc079cbef432335a00017cec0')) paddle.fluid.initializer.MSRAInitializer.__init__ (ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0)), ('document', '53c757bed9345f2ad3361902531e7cf5')) -paddle.fluid.initializer.force_init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '6d0f3e22c90d9d500d36ff57daf056ee')) -paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'a6d7011ca3d8c0d454dac3a56eae0c29')) +paddle.fluid.initializer.force_init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '2c6748c1dd1d85f800462869ea7a747f')) +paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '280b581f5a77e746e47decbc57a7b30a')) paddle.fluid.initializer.NumpyArrayInitializer.__init__ (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None)), ('document', '424e898365195e3ccbc2e7dc8b63605e')) -paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', '89c2c55a0b0656b106064048e068e77a')) -paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)), ('document', 'dfbb624f85015df29e994ca6999e8ff6')) +paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', '6f9f96d2a1517cd1affebc960c3526f7')) +paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)), ('document', '8e35ca26adbe44eb631d71045c8d64d5')) paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None)), ('document', 'b4b608b986eb9617aa0525e1be21d32d')) -paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)), ('document', '4ec4845fd7d991bcac822f8b0dfc101f')) -paddle.fluid.layers.gru_unit (ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)), ('document', 'e0e2439f7af069b57badca18a6ba60b8')) -paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,)), ('document', '7c49ef4bbf0adfd4b9a1d98e2e5f3fea')) +paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)), ('document', '83617c165827e030636c80486d5de6f3')) +paddle.fluid.layers.gru_unit (ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)), ('document', '33974b9bfa69f2f1eb85e6f956dff04e')) +paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,)), ('document', '34f96be41684b0959897a9e735997e20')) paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,)), ('document', '462ddf2435e3392334e0c05ae57a01c4')) paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', 'cefab7c23ee5582727e8b22dffbafac8')) paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', '535f1f6213dd7ca0fe5ed7cb4718c0e3')) -paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '30add751a0f99347a6257634c03ff254')) +paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6263dfdeb6c670fa0922c9cbc8fb1bf4')) paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', 'f273bb26833ee88b349c4b8083e1dc67')) -paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ee152a7ba3036e7b9ede9184545179b4')) -paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None)), ('document', 'b6543768e1afaa2ecb869709d6e9c7e2')) +paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,)), ('document', '5aa25d023acea1fb49a0de56be86990b')) +paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None)), ('document', '3d8e8f3e0e1cf520156be37605e83ccd')) paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '8ca6121acd6d23cd8806a93f493c2e17')) -paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '37042620f9bd3a2da6e5d3138b2f724b')) -paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test'], varargs=None, keywords=None, defaults=(False,)), ('document', 'a194fb80614023f543df3949fbd0d0b8')) +paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', 'd2990494eaf531fb584321b7edfb5104')) +paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', 'e90a93251c52dc4e6fb34fb3991b3f82')) paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '19ef6f9cdd27feac8a1ae060f19c10b4')) -paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', '59b1c6bf2f0fa9dc649c85fef3a3b2ea')) +paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'cee673c79e3ff4582656a24e04f841e5')) paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', 'bbd84e855e660cd1084bb71a2fd0cdaa')) paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', '043de7333b79ee0ac55053c14ed81625')) paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '859b887174d06f361658f69cb7c06d95')) -paddle.fluid.layers.adaptive_pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '120f4323a3d7ed9c0916f15a59f0e497')) -paddle.fluid.layers.batch_norm (ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)), ('document', '581f9f99cd7f4b0cab9e0aad5fa0ea24')) -paddle.fluid.layers.data_norm (ArgSpec(args=['input', 'act', 'epsilon', 'param_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var'], varargs=None, keywords=None, defaults=(None, 1e-05, None, 'NCHW', False, None, None, None, False)), ('document', 'e45e09e65a2658e07cad987222f0d9ab')) -paddle.fluid.layers.beam_search_decode (ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b0b8d53821716cd50c42e09b593f3feb')) +paddle.fluid.layers.adaptive_pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '55db6ae7275fb9678a6814aebab81a9c')) +paddle.fluid.layers.batch_norm (ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)), ('document', '9cf79315d3423dddba0404e8f85a89b8')) +paddle.fluid.layers.data_norm (ArgSpec(args=['input', 'act', 'epsilon', 'param_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var'], varargs=None, keywords=None, defaults=(None, 1e-05, None, 'NCHW', False, None, None, None, False)), ('document', '2460b30fb87037555208fa8ac6fc1787')) +paddle.fluid.layers.beam_search_decode (ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '83e08f21af41ac8bac37aeab1f86fdd0')) paddle.fluid.layers.conv2d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', '03993955ab1e6d3044c44e6f17fc85e9')) paddle.fluid.layers.conv3d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', 'ec113c6a3686ac94f8fccd1a7953d445')) -paddle.fluid.layers.sequence_expand (ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '79c375214fa427faac504043d162dae9')) -paddle.fluid.layers.sequence_expand_as (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9d2611f84ab364c5da545e6a82f1770a')) +paddle.fluid.layers.sequence_expand (ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', 'e91c4b68cc4d8e9f7787b76032a85e75')) +paddle.fluid.layers.sequence_expand_as (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0ecf8d24cf4fd5c035406ee46afccfa0')) paddle.fluid.layers.sequence_pad (ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6a1adf3067b20f6e4bcb354d71c19184')) paddle.fluid.layers.sequence_unpad (ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd12803c903c99aa36ec03aaac5f0cc5b')) -paddle.fluid.layers.lstm_unit (ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None)), ('document', '027723966f3ef0d7bc598f22287a96cc')) -paddle.fluid.layers.reduce_sum (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'b69998ce3ff4980fb21da0df05565f1b')) -paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'd4d80dd98a1a5839f41eeb3a0f85f370')) -paddle.fluid.layers.reduce_max (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '66a622db727551761ce4eb73eaa7f6a4')) -paddle.fluid.layers.reduce_min (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'd50ac552b5d131468ed466d08bb2d38c')) -paddle.fluid.layers.reduce_prod (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'fcd8301a0ce15f219c7a4bcd0c1e8eca')) +paddle.fluid.layers.lstm_unit (ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None)), ('document', 'fe126c58e4339410e875ab1eba246d21')) +paddle.fluid.layers.reduce_sum (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'dd5f06fb7cf39ca06cbab4abd03e6893')) +paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'a3024789eba11a70c2ef27c358173400')) +paddle.fluid.layers.reduce_max (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '10023caec4d7f78c3b901f023a1feaa7')) +paddle.fluid.layers.reduce_min (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '1a1c91625ce3c32646f69ca10d4d1da7')) +paddle.fluid.layers.reduce_prod (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'b386471f0476c80c61d8c8672278063d')) paddle.fluid.layers.reduce_all (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '646ca4d4a2cc16084f59de44b6927eca')) paddle.fluid.layers.reduce_any (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'f36661060aeeaf6c6b1331e41b3726fa')) paddle.fluid.layers.sequence_first_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '2b290d3d77882bfe9bb8d331cac8cdd3')) paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'c16a892f44f7fe71bfa5afc32d3f34ce')) paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'fdcea0e8b5bc7d8d4b1b072c521014e6')) paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', 'f1dd22f7351f7f9853212958e0d8aa7a')) -paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '652625345c2acb900029c78cc75f8aa6')) +paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '59b28903ce8fb6a7e3861ff355592eb4')) paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2bc3a59efa9d52b628a6255422d9f0e8')) -paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None)), ('document', '97f0262f97602644c83142789d784571')) +paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None)), ('document', 'f2c252aa2f83f8e503ffaf79668eaa28')) paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', '35c6a241bcc1a1fc89508860d82ad62b')) -paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', 'b4cbe1ac451005df6dad12e9ffdccca9')) -paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd3570c02f71bcd78e60b3f31dc8f5b32')) -paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False)), ('document', 'aaba49c038ba927f0a8e45c0c9a686ab')) -paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'a10ab9bf88d4a7e328882d411abb6fd1')) -paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a1feac48b843d679db82312dc85885f4')) -paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', '3ce01160ede80b1c26f776f8fef9340f')) +paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', 'aa27ca4405e70c6a733cb9806a76af30')) +paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2a1e9ea041ff4d6a9948bb8d03b743ea')) +paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False)), ('document', '4aa9df890b47eb67d5442f04aaf9eeec')) +paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'f568714a876425004aca4ea2d4a27701')) +paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8e72db173d4c082e27cb11f31d8c9bfa')) +paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', '33134416fc27dd65a767e5f15116ee16')) paddle.fluid.layers.nce (ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)), ('document', '32b3c442da0f3df682b5fcac10468116')) -paddle.fluid.layers.sampled_softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0)), ('document', '5db30b8a74e8c93687943a3e8d221da0')) -paddle.fluid.layers.hsigmoid (ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)), ('document', '80641ee6810b1cdc3fd6e14fc89ecc9d')) -paddle.fluid.layers.beam_search (ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False)), ('document', 'b350b9a30a18e7efd7e1bb740eef6996')) +paddle.fluid.layers.sampled_softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0)), ('document', '4521da36af223d5a95bb8f190b5c7add')) +paddle.fluid.layers.hsigmoid (ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)), ('document', 'b83e7dfa81059b39bb137922dc914f50')) +paddle.fluid.layers.beam_search (ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False)), ('document', '1270395ce97a4e1b556104abbb14f096')) paddle.fluid.layers.row_conv (ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None)), ('document', '17485788fffe4e2d36dc58c2ac8d174e')) paddle.fluid.layers.multiplex (ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None), ('document', '2c4d1ae83da6ed35e3b36ba1b3b51d23')) paddle.fluid.layers.layer_norm (ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None)), ('document', 'de6a906950bae9f3c245cb744d22b94e')) paddle.fluid.layers.group_norm (ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None)), ('document', '419c3a24a83cc89219a029cf4092788b')) -paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters', 'eps', 'name'], varargs=None, keywords=None, defaults=(0, 1, 1e-12, None)), ('document', '3f536aafba30d793287b52d231baff1b')) +paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters', 'eps', 'name'], varargs=None, keywords=None, defaults=(0, 1, 1e-12, None)), ('document', '9461e67095a6fc5d568fb2ce8fef66ff')) paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax', 'axis'], varargs=None, keywords=None, defaults=(False, -100, True, False, -1)), ('document', '8b074f9c56b4233a2b65d03254eb309e')) paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'c6b175d253c55baf4b9c0eca9b1dda88')) paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None), ('document', '960fc799549c202da1e85d626cb2c962')) paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', '67afefa80b6cc38801bd5b631fed8a4a')) paddle.fluid.layers.reshape (ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', '323c019f257e55ddea4a824a362de62f')) -paddle.fluid.layers.squeeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3229d06517f794e86ca3da14c38b1465')) -paddle.fluid.layers.unsqueeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bbd62da391b1df984a1909d069a759b2')) -paddle.fluid.layers.lod_reset (ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'f122194c562bd674f6ecdccf33785f99')) +paddle.fluid.layers.squeeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '06588973f613e9dcd592724322864589')) +paddle.fluid.layers.unsqueeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b9bd3129d36a70e7c4385df51ff71c62')) +paddle.fluid.layers.lod_reset (ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None)), ('document', '395e6ba041ccfacfe1d534c3e107fd66')) paddle.fluid.layers.lrn (ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None)), ('document', '330241f0bc57e9d16973ec322a6aef71')) paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '2f189f8ef61f1c23779e1593b78755c0')) paddle.fluid.layers.pad_constant_like (ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '95aa1972983f30fe9b5a3713e523e20f')) -paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '70c113658102a11cc5d8e3d45145737a')) -paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', 'c317aa595deb31649083c8faa91cdb97')) +paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '9060f4cab873c4ab2deed5211080698e')) +paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', 'ceedc8c22752c623d6e1ea2e8df0f43f')) paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '3d8f4891c1d5e890a4e574371027dd35')) paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,)), ('document', '7e8e4bf1f0f8612961ed113e8af8f0c5')) paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1)), ('document', 'f1bc5eb7198175d2b79197a681d98b43')) paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', '099b9f051e6247ae661e4a7b4fd3f89a')) paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1)), ('document', '746bf58fdb1bd475f8c5f996b05b0e52')) paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners'], varargs=None, keywords=None, defaults=(None, None, None, None, True)), ('document', '9baf9288c862161ff850d45228047a5e')) -paddle.fluid.layers.gather (ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None), ('document', '01a198d6fff38d5f0d8180a40b228085')) -paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65f8e9d8ddfd0b412f940579c4faa342')) -paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '15b522457dfef103f0c20ca9d397678b')) +paddle.fluid.layers.gather (ArgSpec(args=['input', 'index', 'overwrite'], varargs=None, keywords=None, defaults=(True,)), ('document', '3569a6002a96c7f6b5e5bcfdc402df13')) +paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name', 'overwrite'], varargs=None, keywords=None, defaults=(None, True)), ('document', '69b22affd4a6326502af166f04c095ab')) +paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '71df5136cf03b06c65027b692fe78f1a')) paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c9ab9e460ef0a1823249935a30e82c66')) -paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', '35cbbdfa585d027bb490707c95a176b9')) +paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', 'e3b6630ba43cb13dfeeb1601cb64d671')) paddle.fluid.layers.relu (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bf1676268df8ef100b8ab01d51336b25')) -paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '9044c7fe667b76cb2d9264f2db11f417')) -paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '98247c59d1c9b40af6730001b2aea73d')) +paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'f93c61f5b0bf933cd425a64dca2c4fdd')) +paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '056af2c0e6e22d94e8df7fc39677707f')) paddle.fluid.layers.crop (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'ddf9837ee83e549119210a3d714d5f44')) -paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c542e39ac6add24a6bef6e79bf5617e2')) +paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1098b7a70c7696cc7437d6d57b5d89ed')) paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '99b3fee0daee04911d2bee8871b26435')) paddle.fluid.layers.elu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '463258ee9f8b60760eb1e26357cc9bfa')) paddle.fluid.layers.relu6 (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None)), ('document', '6f367339caf6c7124bc262fe1475df70')) -paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'a5117c1eb84aca2ac0b0abab337a4799')) +paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '35fa2b79b1ae6968d4a69788051c1d27')) paddle.fluid.layers.stanh (ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.6666666666666666, 1.7159, None)), ('document', '959936a477efc6c1447a9c8bf8ce94bb')) -paddle.fluid.layers.hard_sigmoid (ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None)), ('document', 'c82059b6fea1aa730f9aac911807b756')) +paddle.fluid.layers.hard_sigmoid (ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None)), ('document', '607d79ca873bee40eed1c79a96611591')) paddle.fluid.layers.swish (ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'ef745e55a48763ee7b46b21a81dc7e84')) -paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'f6acef7ff7d887e49ff499fbb1dad4a9')) +paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '2da40e447716338affebfe058d05d9a9')) paddle.fluid.layers.brelu (ArgSpec(args=['x', 't_min', 't_max', 'name'], varargs=None, keywords=None, defaults=(0.0, 24.0, None)), ('document', '3db337c195e156e6ef2b8b4a57113600')) paddle.fluid.layers.leaky_relu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(0.02, None)), ('document', 'f878486c82b576938151daad0de995a0')) -paddle.fluid.layers.soft_relu (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(40.0, None)), ('document', '869adce548c342d6cc1bd88a948d83c9')) +paddle.fluid.layers.soft_relu (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(40.0, None)), ('document', '3490ed5c9835ae039a82979daf3918a4')) paddle.fluid.layers.flatten (ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'cb295c13cb957db85cd9609269d7784d')) -paddle.fluid.layers.sequence_mask (ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None)), ('document', 'f0dd6eddd3bff015a3c05269d82fcbd8')) -paddle.fluid.layers.stack (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '367cfbb642839beacb5d117e2d2b4041')) -paddle.fluid.layers.pad2d (ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None)), ('document', '7f4d46320cc077ca2e8db600c35f4030')) -paddle.fluid.layers.unstack (ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None)), ('document', '98eb9d633116efcfc6f90c114bd44fd6')) -paddle.fluid.layers.sequence_enumerate (ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'f6028537085dc296103bbbd85fa7763d')) +paddle.fluid.layers.sequence_mask (ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None)), ('document', '767cea598dee8e2b94f04110fa6b7e67')) +paddle.fluid.layers.stack (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', 'e8d86c47e92bcb878ff8022b6f66cec2')) +paddle.fluid.layers.pad2d (ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None)), ('document', '3f3abdb795a5c2aad8c2312249551ce5')) +paddle.fluid.layers.unstack (ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'b0c4ca08d4eb295189e1b107c920d093')) +paddle.fluid.layers.sequence_enumerate (ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', '2e49e97069beb57ee89d54ed088ae2da')) paddle.fluid.layers.expand (ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '117d3607d1ffa0571835bbaebc7857ff')) -paddle.fluid.layers.sequence_concat (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3a1d155dd1bf6e72a0a3e3e1519591d1')) -paddle.fluid.layers.scale (ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None)), ('document', '30190413b2fa442e7466d6cf2ce5ea07')) -paddle.fluid.layers.elementwise_add (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '210ee7e597f429f836a21b298991ef85')) -paddle.fluid.layers.elementwise_div (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '9ce91719cf4a05de9a817e9ff2387ee8')) -paddle.fluid.layers.elementwise_sub (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'c66c50b550bc547b6c61d15c1f3ee2ab')) -paddle.fluid.layers.elementwise_mul (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'e6919013e5369c7b0d486b8604da6b2f')) -paddle.fluid.layers.elementwise_max (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'f839de1318c794f26b9f5aafcd2ad92f')) -paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'c37aa719815585f2c20623f92e738d54')) -paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '984e0e72db2a3b4241a694499f8d76c8')) -paddle.fluid.layers.elementwise_mod (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '4aa6b682b8676a2f3adf9f58790e327d')) -paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '638ca44932743bda05caf3fcc15f1f0d')) -paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', '129e0a3257f1d532a948eedf9d5bf671')) -paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '389dafe36e099841b6a7fb18d11f1b4c')) +paddle.fluid.layers.sequence_concat (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b992616c1afbd6b0c2a897ac23036381')) +paddle.fluid.layers.scale (ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None)), ('document', '463e4713806e5adaa4d20a41e2218453')) +paddle.fluid.layers.elementwise_add (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '5c0fb7298aec32525f96d451ae4c2851')) +paddle.fluid.layers.elementwise_div (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '1da49b7cda887dd84087ef8c060fcf6a')) +paddle.fluid.layers.elementwise_sub (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '992559c8327c61babd2ed25fc9047fbf')) +paddle.fluid.layers.elementwise_mul (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '213db11a61dcb0f31159d343cc35e2f5')) +paddle.fluid.layers.elementwise_max (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '409167a1409ec31b0d3a2f8852a7943f')) +paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '4e1322836eb69473d5606bfe346c5375')) +paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'b9e7e9fa1ca28d8b6f07cc59eadb4a02')) +paddle.fluid.layers.elementwise_mod (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '614984304f810f3ddae6b489ec01296b')) +paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'a8c4b26d899246378e878f169582c7a4')) +paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', 'c8c7518358cfbb3822a019e6b5fbea52')) +paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '8c78ccb77e291e4a0f0673d34823ce4b')) paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '35428949368cad5121dd37f8522ef8b0')) paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '9e520987168f8ddb7dd71ffd68aa352c')) -paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'a418e3ccb5e2ac21bd60f5cc221d5860')) -paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '01dbb91e7c74cb11336cd531013de51a')) -paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '17db0f814eb7bb5a3fac1ca6e60e16d8')) +paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '4527fd90e222f67b5f7451fb0cf7c845')) +paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '3ca6a761570d86e303e473afba99bb49')) +paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'bf61c8f79d795a8371bdb3b5468aa82b')) paddle.fluid.layers.rank (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'ee1386c42ecc8f424fe3fb21862fefc2')) paddle.fluid.layers.logical_and (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cdcf20c494c92060d10feb9374532f42')) paddle.fluid.layers.logical_or (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '0eae3f726a4afe590757552fa3ced012')) @@ -206,44 +207,48 @@ paddle.fluid.layers.logical_xor (ArgSpec(args=['x', 'y', 'out', 'name'], varargs paddle.fluid.layers.logical_not (ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cd1c8cf31e040427d4e05711044caeb6')) paddle.fluid.layers.clip (ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0ce33756573c572da67302499455dbcd')) paddle.fluid.layers.clip_by_norm (ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a1ea0bc5a926f427458c4254ca022749')) -paddle.fluid.layers.mean (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd638d915195ce86a8d7963b81110d4c8')) -paddle.fluid.layers.mul (ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None)), ('document', 'ccd37fa6b53f074adbfb732d738c4c2d')) +paddle.fluid.layers.mean (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9562845452b0455fa23ab64334415417')) +paddle.fluid.layers.mul (ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None)), ('document', '784b7e36cea88493f9e37a41b10fbf4d')) paddle.fluid.layers.sigmoid_cross_entropy_with_logits (ArgSpec(args=['x', 'label', 'ignore_index', 'name', 'normalize'], varargs=None, keywords=None, defaults=(-100, None, False)), ('document', '180c284317ea45ef89a460d8d79c0b72')) paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '71426e02d240d0daedae81a02ca1c191')) -paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a9221eaef53884a00654e028551b78e2')) +paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'aba90d0cbb43185216000b82fd231734')) paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f85b263b7b6698d000977529a28f202b')) -paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '77a6d80aa5551ca70324fc975c44507f')) -paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', 'ab84fdc6dc60f3ad9aa397e6007e3bf9')) +paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65c8362e48810b8226e311c5d046db51')) +paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', '9f303c67538e468a36c5904a0a3aa110')) paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6f90d6ff76bf4f5e592332c1ef28494e')) -paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', '97bf4353bb046a5629308a38f98ac204')) -paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd256cba1c41a5ed92ce3f31e24a2ca6d')) +paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'da621ba1363e8f5fe7b702526bbae18f')) +paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5d16663e096d7f04954c70ce1cc5e195')) paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)), ('document', 'af541e9263be61ce0e40df58d1b69294')) -paddle.fluid.layers.add_position_encoding (ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '4b9c2e8af5817937d831820874b5aa77')) +paddle.fluid.layers.add_position_encoding (ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e399f9436fed5f7ff480d8532e42c937')) paddle.fluid.layers.bilinear_tensor_product (ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'cd0bd55ef1e1762aca25ec972d34d378')) -paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'dc63315b84f591ac79ecca0c3632027a')) -paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7ffc849e71f31dfe29030ff94e662de6')) -paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', 'd5e6c494ac35100e2ed4d4bd9a1ed932')) +paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c2a9c00d5c22e156d92ffa2e8736adf3')) +paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3e60aec040a6f740a130353323580bff')) +paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', '4e513cbd7c8d0d64e426dbbc94cb72b7')) paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2fa6782d43d02ae64482d21235a82949')) paddle.fluid.layers.temporal_shift (ArgSpec(args=['x', 'seg_num', 'shift_ratio', 'name'], varargs=None, keywords=None, defaults=(0.25, None)), ('document', 'fe4481fb31363b09cfdd228fc6776ddf')) paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '8404e472ac12b4a30a505d3d3a3e5fdb')) paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '42d5155374f69786300d90d751956998')) -paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', '2f6ff96864054a31aa4bb659c6722c99')) -paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None), ('document', '431a4301c35032166ec029f7432c80a7')) +paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', '07cb0d95a646dba1b9cc7cdce89e59f0')) +paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None), ('document', '11bb8e62cc9256958eff3991fe4834da')) paddle.fluid.layers.kldiv_loss (ArgSpec(args=['x', 'target', 'reduction', 'name'], varargs=None, keywords=None, defaults=('mean', None)), ('document', '776d536cac47c89073abc7ee524d5aec')) paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None)), ('document', '2985a372ac897ea4e13aced7f930d6f8')) paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', '46994d10276dd4cb803b4062b5d14329')) paddle.fluid.layers.pixel_shuffle (ArgSpec(args=['x', 'upscale_factor'], varargs=None, keywords=None, defaults=None), ('document', '132b6e74ff642a392bd6b14c10aedc65')) -paddle.fluid.layers.fsp_matrix (ArgSpec(args=['x', 'y'], varargs=None, keywords=None, defaults=None), ('document', 'b76ccca3735bea4a58a0dbf0d77c5393')) -paddle.fluid.layers.continuous_value_model (ArgSpec(args=['input', 'cvm', 'use_cvm'], varargs=None, keywords=None, defaults=(True,)), ('document', 'a07a44c2bacdcd09c1f5f35a96a0514e')) +paddle.fluid.layers.fsp_matrix (ArgSpec(args=['x', 'y'], varargs=None, keywords=None, defaults=None), ('document', '20992b20d19c2e5983f366150827b4a6')) +paddle.fluid.layers.continuous_value_model (ArgSpec(args=['input', 'cvm', 'use_cvm'], varargs=None, keywords=None, defaults=(True,)), ('document', '94e2819b7c9715ea71b62e9c78f36b29')) paddle.fluid.layers.where (ArgSpec(args=['condition'], varargs=None, keywords=None, defaults=None), ('document', '3126e3039e752ce26077f1efaca355c6')) -paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', 'adf285346e23316097f7789b572491e9')) -paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)), ('document', 'cf12066a3139026119f97f9d4381a1bd')) -paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'b0a1c2fc51c27a106da28f3308c41f5e')) -paddle.fluid.layers.shuffle (ArgSpec(args=['reader', 'buffer_size'], varargs=None, keywords=None, defaults=None), ('document', 'f967a73426db26f970bc70bfb03cffca')) +paddle.fluid.layers.sign (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'ccf6bb7912afd2818d24bc45461e807a')) +paddle.fluid.layers.deformable_conv (ArgSpec(args=['input', 'offset', 'mask', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'deformable_groups', 'im2col_step', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, None, None, None)), ('document', 'c896b66265a60bd3c5510f66e6e02919')) +paddle.fluid.layers.unfold (ArgSpec(args=['x', 'kernel_sizes', 'strides', 'paddings', 'dilations', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None)), ('document', '3f884662ad443d9ecc2b3734b4f61ad6')) +paddle.fluid.layers.deformable_roi_pooling (ArgSpec(args=['input', 'rois', 'trans', 'no_trans', 'spatial_scale', 'group_size', 'pooled_height', 'pooled_width', 'part_size', 'sample_per_part', 'trans_std', 'position_sensitive', 'name'], varargs=None, keywords=None, defaults=(False, 1.0, [1, 1], 1, 1, None, 1, 0.1, False, None)), ('document', '65b8dbe13e00c4dc8224652f6ff89540')) +paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '9e87163ba32003f21d2c9d8c6a605ada')) +paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)), ('document', 'cccb6eb5410c822e5307c947aca2c899')) +paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', '32181f6037e387fb6e68a5beaafe33b6')) +paddle.fluid.layers.shuffle (ArgSpec(args=['reader', 'buffer_size'], varargs=None, keywords=None, defaults=None), ('document', 'f29d7d159e114f73fc988d9a86805841')) paddle.fluid.layers.batch (ArgSpec(args=['reader', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', 'fcb24383c6eef2ca040ee824c26e22fd')) -paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '07e5b796674796eb1ef3fee9c10d24e3')) +paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'c13b8a8521bea5f8123b925ae2a5d5db')) paddle.fluid.layers.random_data_generator (ArgSpec(args=['low', 'high', 'shapes', 'lod_levels', 'for_parallel'], varargs=None, keywords=None, defaults=(True,)), ('document', '9b7f0f86ec24bbc97643cadcb6499cff')) -paddle.fluid.layers.py_reader (ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', '5c54493d96c7e0760dc6758af1c8dd72')) +paddle.fluid.layers.py_reader (ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', '0a77c56dff556b5ae4c5630d9a0764ef')) paddle.fluid.layers.create_py_reader_by_data (ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True)), ('document', 'b42332b894e1e0962c6a43f0151c2640')) paddle.fluid.layers.Preprocessor.__init__ (ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.layers.Preprocessor.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) @@ -251,24 +256,24 @@ paddle.fluid.layers.Preprocessor.inputs (ArgSpec(args=['self'], varargs=None, ke paddle.fluid.layers.Preprocessor.outputs (ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.layers.load (ArgSpec(args=['out', 'file_path', 'load_as_fp16'], varargs=None, keywords=None, defaults=(None,)), ('document', '9d1a4bc97bbce9fa1d4f7a4200a771ff')) paddle.fluid.layers.create_tensor (ArgSpec(args=['dtype', 'name', 'persistable'], varargs=None, keywords=None, defaults=(None, False)), ('document', 'c0c3d0194f83fff8ea99ce0820657dae')) -paddle.fluid.layers.create_parameter (ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'd62b866c899bc1fedb5385f95b88e1f8')) -paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None)), ('document', 'ab914fac893607e29ac6e52bbdbea1a4')) +paddle.fluid.layers.create_parameter (ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'b6fe28cffff32d15e45c411bcf815cb7')) +paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None)), ('document', '90eb79e0d1261ec2bac7c775ee4f459b')) paddle.fluid.layers.cast (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '992eb42590fc1c380841a6db72ce78b3')) -paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'b12717d3d4567e6119589f7f655b0cbb')) +paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', '764c095ba4562ae740f979e970152d6e')) paddle.fluid.layers.concat (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'f9e905b48123914c78055a45fe23106a')) -paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '42912092418620b4be07f36af31e7816')) -paddle.fluid.layers.assign (ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b690184f3537df5501e4d9d8f31152a5')) -paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0)), ('document', 'd4059a2f5763036b07018d76429f9acb')) -paddle.fluid.layers.fill_constant (ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None)), ('document', '1d8b14729639fa38509c79b9784740fa')) +paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '5df743d578638cd2bbb9369499b44af4')) +paddle.fluid.layers.assign (ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,)), ('document', '8bd94aef4e123986d9a8c29f67b5532b')) +paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0)), ('document', 'baf63a2f3b647a2d5da6ba8afb6135ac')) +paddle.fluid.layers.fill_constant (ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'd6b76c7d2c7129f8d713ca74f1c2c287')) paddle.fluid.layers.argmin (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '677c09cc0fd7381974bfc845c4d9f0f2')) paddle.fluid.layers.argmax (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', 'ef64ee883998e7e246a854a845e11e2c')) paddle.fluid.layers.argsort (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '0a85a9a145d2e24e05958a3f1322d68a')) -paddle.fluid.layers.ones (ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)), ('document', 'b402489c62e668df42e7daceb63c142b')) -paddle.fluid.layers.zeros (ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)), ('document', 'c155e2efc56ffa5ed4658cca0272e491')) -paddle.fluid.layers.reverse (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None), ('document', '8ee7cb6ca639e7460e825f953b65d94d')) -paddle.fluid.layers.has_inf (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '8f8c0306117ea441f20dcbbdba1f0ecc')) -paddle.fluid.layers.has_nan (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '2e53e83127dbfd86e7098bdfe9a549e8')) -paddle.fluid.layers.isfinite (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '0a437011c3906079fd8947ed3e52d292')) +paddle.fluid.layers.ones (ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)), ('document', '812c623ed52610b9773f9fc05413bc34')) +paddle.fluid.layers.zeros (ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)), ('document', '95379f9288c2d05356ec0e2375c6bc57')) +paddle.fluid.layers.reverse (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None), ('document', '628135603692137d52bcf5a8d8d6816d')) +paddle.fluid.layers.has_inf (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '51a0fa1cfaf2507c00a215adacdb8a63')) +paddle.fluid.layers.has_nan (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '129cf426e71452fe8276d616a6dc21ae')) +paddle.fluid.layers.isfinite (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '548a0ae317105e6dbfed321d7e37c03d')) paddle.fluid.layers.range (ArgSpec(args=['start', 'end', 'step', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '2ec937ede953ded2fdff2675883900bb')) paddle.fluid.layers.linspace (ArgSpec(args=['start', 'stop', 'num', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '495e21e9a848c2d075a102802fc67756')) paddle.fluid.layers.zeros_like (ArgSpec(args=['x', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c7e4cfffc93ae89c8f6f53b6d650f923')) @@ -276,19 +281,19 @@ paddle.fluid.layers.diag (ArgSpec(args=['diagonal'], varargs=None, keywords=None paddle.fluid.layers.While.__init__ (ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.layers.While.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.layers.Switch.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.layers.Switch.case (ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None), ('document', 'f7c7160014c1b46cfeda9dd5808d1789')) -paddle.fluid.layers.Switch.default (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '50853ae884df03d9c36703bb46d9ef07')) -paddle.fluid.layers.increment (ArgSpec(args=['x', 'value', 'in_place'], varargs=None, keywords=None, defaults=(1.0, True)), ('document', '73bb96ec4783ec1a11e760e8851b0e77')) -paddle.fluid.layers.array_write (ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,)), ('document', '40b6d15f4c86b2b09df340d7778ad713')) +paddle.fluid.layers.Switch.case (ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.Switch.default (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.layers.increment (ArgSpec(args=['x', 'value', 'in_place'], varargs=None, keywords=None, defaults=(1.0, True)), ('document', 'f88b5787bb80ae6b8bf513a70dabbdc1')) +paddle.fluid.layers.array_write (ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,)), ('document', '3f913b5069ad40bd85d89b33e4aa5939')) paddle.fluid.layers.create_array (ArgSpec(args=['dtype'], varargs=None, keywords=None, defaults=None), ('document', '2d4f20087080ba5105b55205ad5c5b6a')) -paddle.fluid.layers.less_than (ArgSpec(args=['x', 'y', 'force_cpu', 'cond'], varargs=None, keywords=None, defaults=(None, None)), ('document', '067bbc799c66289ca8b8924c26b6673f')) +paddle.fluid.layers.less_than (ArgSpec(args=['x', 'y', 'force_cpu', 'cond'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'eb41e5993f705fcfa354024054a75f5f')) paddle.fluid.layers.less_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd6b173ae1a149e0bdfe7b8bf69285957')) paddle.fluid.layers.greater_than (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '2c9bd414caa6c615539018d27001b44c')) paddle.fluid.layers.greater_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '62c667d24e7b07e166b47a53b61b2ff4')) -paddle.fluid.layers.equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '80c29b1dc64718f0116de90d1ac88a77')) +paddle.fluid.layers.equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '788aa651e8b9fec79d16931ef3a33e90')) paddle.fluid.layers.not_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '56148fb1024687a08e96af79bdc5c929')) -paddle.fluid.layers.array_read (ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None), ('document', 'dd68bead34dfbaf6b0a163fc1cc3c385')) -paddle.fluid.layers.array_length (ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None), ('document', 'ffb8b9578ec66db565b223d313aa82a2')) +paddle.fluid.layers.array_read (ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None), ('document', 'caf0d94349cdc28e1bda3b8a19411ac0')) +paddle.fluid.layers.array_length (ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None), ('document', '6f24a9b872027634ad758ea2826c9727')) paddle.fluid.layers.IfElse.__init__ (ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.layers.IfElse.false_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.layers.IfElse.input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) @@ -296,21 +301,21 @@ paddle.fluid.layers.IfElse.output (ArgSpec(args=['self'], varargs='outs', keywor paddle.fluid.layers.IfElse.true_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.layers.DynamicRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.layers.DynamicRNN.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6d3e0a5d9aa519a9773a36e1620ea9b7')) -paddle.fluid.layers.DynamicRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32')), ('document', 'b9174d4e91505b0c8ecc193eb51e248d')) +paddle.fluid.layers.DynamicRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32')), ('document', '57cdd0a63747f4c670cdb9d250ceb7e1')) paddle.fluid.layers.DynamicRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', 'b439a176a3328de8a75bdc5c08eece4a')) -paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', 'f29ad2478b6b2ad4f413d2936a331ea0')) -paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x', 'level'], varargs=None, keywords=None, defaults=(0,)), ('document', '7568c5ac7622a10288d3307a94134655')) +paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '55ab9c562edd7dabec0bd6fd6c1a28cc')) +paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x', 'level'], varargs=None, keywords=None, defaults=(0,)), ('document', '4b300851b5201891d0e11c406e4c7d07')) paddle.fluid.layers.DynamicRNN.update_memory (ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None), ('document', '5d83987da13b98363d6a807a52d8024f')) paddle.fluid.layers.StaticRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.layers.StaticRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)), ('document', '72530f299d6451a567cf4a12dc3fb1ff')) +paddle.fluid.layers.StaticRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)), ('document', 'f1b60dc4194d0bb714d6c6f5921b227f')) paddle.fluid.layers.StaticRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', 'df6ceab6e6c9bd31e97914d7e7538137')) paddle.fluid.layers.StaticRNN.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6d3e0a5d9aa519a9773a36e1620ea9b7')) paddle.fluid.layers.StaticRNN.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '903387ec11f3d0bf46821d31a68cffa5')) paddle.fluid.layers.StaticRNN.step_output (ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None), ('document', '252890d4c3199a7623ab8667e13fd837')) paddle.fluid.layers.StaticRNN.update_memory (ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None), ('document', '7a0000520f179f35239956a5ba55119f')) -paddle.fluid.layers.reorder_lod_tensor_by_rank (ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None), ('document', '3545f529ef04e8f6ecb76b47fa3df01a')) -paddle.fluid.layers.Print (ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both')), ('document', '5fef91b0e21c93610785f2b1f7161732')) -paddle.fluid.layers.is_empty (ArgSpec(args=['x', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bbe578dbb49ad13e15b014e98c22b519')) +paddle.fluid.layers.reorder_lod_tensor_by_rank (ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None), ('document', '5b552a1f0f7eb4dacb768a975ba15d08')) +paddle.fluid.layers.Print (ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both')), ('document', 'ee6c70867d317b0a87094ed23546215f')) +paddle.fluid.layers.is_empty (ArgSpec(args=['x', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '3011dc695f490afdf504dc24f628319a')) paddle.fluid.layers.sigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a4e395ab004e7da34e94a0a1f9eee183')) paddle.fluid.layers.logsigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5f2508c52e0a797bb9bd5e29d79ede78')) paddle.fluid.layers.exp (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '41c976b68542f4cbee178640f765d845')) @@ -332,38 +337,41 @@ paddle.fluid.layers.reciprocal (ArgSpec(args=['x', 'name'], varargs=None, keywor paddle.fluid.layers.square (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'fcc0d8ec2d2983f5d2ae0196fa83916b')) paddle.fluid.layers.softplus (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a9bef6674dc20af1ae901656ed041cdf')) paddle.fluid.layers.softsign (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5c1e9c619db82d6392826d0c2908ea55')) -paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', 'a8c4e972b7d6742c838a37abf407ed9a')) +paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '6de6775d9e9ed885056e764982130cfd')) paddle.fluid.layers.hard_shrink (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c142f5884f3255e0d6075c286bbd531e')) paddle.fluid.layers.cumsum (ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '944d7c03057f5fc88bc78acd4d82f926')) paddle.fluid.layers.thresholded_relu (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '90566ea449ea4c681435546e2f70610a')) paddle.fluid.layers.prior_box (ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False)), ('document', 'a00d43a08ec664454e8e685bc54e9e78')) paddle.fluid.layers.density_prior_box (ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None)), ('document', '7e62e12ce8b127f2c7ce8db79299c3c3')) -paddle.fluid.layers.multi_box_head (ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False)), ('document', 'fe9afaee481dd09f28866df22756466f')) +paddle.fluid.layers.multi_box_head (ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False)), ('document', 'fd58078fdfffd899b91f992ba224628f')) paddle.fluid.layers.bipartite_match (ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '3ddb9b966f193900193a95a3df77c3c1')) -paddle.fluid.layers.target_assign (ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'c0b334f917828f95056f6ebe10907b1c')) -paddle.fluid.layers.detection_output (ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0)), ('document', 'c33093a82a46e3091e789e5572588db1')) +paddle.fluid.layers.target_assign (ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'e9685f32d21bec8c013626c0254502c5')) +paddle.fluid.layers.detection_output (ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0)), ('document', 'efae414c1137c7944d6174dd08c5347a')) paddle.fluid.layers.ssd_loss (ArgSpec(args=['location', 'confidence', 'gt_box', 'gt_label', 'prior_box', 'prior_box_var', 'background_label', 'overlap_threshold', 'neg_pos_ratio', 'neg_overlap', 'loc_loss_weight', 'conf_loss_weight', 'match_type', 'mining_type', 'normalize', 'sample_size'], varargs=None, keywords=None, defaults=(None, 0, 0.5, 3.0, 0.5, 1.0, 1.0, 'per_prediction', 'max_negative', True, None)), ('document', '6d5028fd09d01ab82d296adc0ea95aee')) -paddle.fluid.layers.detection_map (ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral')), ('document', '1467d91b50c22cd52103b4aa1ee9d0a1')) -paddle.fluid.layers.rpn_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True)), ('document', '1dddef3eb4b3cbd4df8e03ac480dbf97')) +paddle.fluid.layers.rpn_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True)), ('document', '1e164a56fe9376e18a56d22563d9f801')) +paddle.fluid.layers.retinanet_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'gt_labels', 'is_crowd', 'im_info', 'num_classes', 'positive_overlap', 'negative_overlap'], varargs=None, keywords=None, defaults=(1, 0.5, 0.4)), ('document', 'fa1d1c9d5e0111684c0db705f86a2595')) +paddle.fluid.layers.sigmoid_focal_loss (ArgSpec(args=['x', 'label', 'fg_num', 'gamma', 'alpha'], varargs=None, keywords=None, defaults=(2, 0.25)), ('document', 'aeac6aae100173b3fc7f102cf3023a3d')) paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', '82b2aefeeb1b706bc4afec70928a259a')) -paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', '9307c12b1d4e554279b9708f787cd019')) -paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True)), ('document', '87863717edeb7fe87a1268976cbc015d')) -paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', '57ab49f3f324f310b7eed322e7c1057a')) -paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'f73706a65468e9ca3e0bee4a31521b0a')) -paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '587845f60c5d97ffdf2dfd21da52eca1')) +paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', 'd1ddc75629fedee46f82e631e22c79dc')) +paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random', 'is_cls_agnostic', 'is_cascade_rcnn'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True, False, False)), ('document', 'c0d00acf724691ff3480d4207036a722')) +paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', 'b7d707822b6af2a586bce608040235b1')) +paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'b319b10ddaf17fb4ddf03518685a17ef')) +paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '72fca4a39ccf82d5c746ae62d1868a99')) paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '032d0f4b7d8f6235ee5d91e473344f0e')) -paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0e5ac2507723a0b5adec473f9556799b')) +paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e308ce1661cb722b220a6f482f85b9e4')) paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', 'eb62b1ff7cc981f3483a62321a491f2e')) paddle.fluid.layers.yolo_box (ArgSpec(args=['x', 'img_size', 'anchors', 'class_num', 'conf_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f332fb8c5bb581bd1a6b5be450a99990')) paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '04384378ff00a42ade8fabd52e27cbc5')) paddle.fluid.layers.multiclass_nms (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)), ('document', 'ca7d1107b6c5d2d6d8221039a220fde0')) +paddle.fluid.layers.retinanet_detection_output (ArgSpec(args=['bboxes', 'scores', 'anchors', 'im_info', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0.05, 1000, 100, 0.3, 1.0)), ('document', '078d28607ce261a0cba2b965a79f6bb8')) paddle.fluid.layers.distribute_fpn_proposals (ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7bb011ec26bace2bc23235aa4a17647d')) paddle.fluid.layers.box_decoder_and_assign (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'box_score', 'box_clip', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'dfc953994fd8fef35c49dd9c6eea37a5')) -paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', '9808534c12c5e739a10f73ebb0b4eafd')) -paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', 'e0e95334fce92d16c2d9db6e7caffc47')) -paddle.fluid.layers.exponential_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', '98a5050bee8522fcea81aa795adaba51')) -paddle.fluid.layers.natural_exp_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', '676a7bc2a218691db50bca233903d21e')) -paddle.fluid.layers.inverse_time_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'd07e767d59c4a5e6c930f3e6756d3f82')) +paddle.fluid.layers.collect_fpn_proposals (ArgSpec(args=['multi_rois', 'multi_scores', 'min_level', 'max_level', 'post_nms_top_n', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '82ffd896ecc3c005ae1cad40854dcace')) +paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', 'ef799022a6040597462ae2b3d2f1c407')) +paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', '300537e259bba86fdefa13a133a0587d')) +paddle.fluid.layers.exponential_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'eaf430c5a0380fb11bfe9a8922cd6295')) +paddle.fluid.layers.natural_exp_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', '63a9e96d446d7de1289f30b832bce36a')) +paddle.fluid.layers.inverse_time_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'ea37a3a8a0b3ce2254e7bc49a0951dbe')) paddle.fluid.layers.polynomial_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False)), ('document', 'a343254c36c2e89512cd8cd8a1960ead')) paddle.fluid.layers.piecewise_decay (ArgSpec(args=['boundaries', 'values'], varargs=None, keywords=None, defaults=None), ('document', 'd9f654117542c6b702963dda107a247f')) paddle.fluid.layers.noam_decay (ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None), ('document', 'f96805b1a64f9a12f4627497e5fcb920')) @@ -398,8 +406,9 @@ paddle.fluid.contrib.QuantizeTranspiler.training_transpile (ArgSpec(args=['self' paddle.fluid.contrib.Calibrator.__init__ (ArgSpec(args=['self'], varargs='args', keywords='kwargs', defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.contrib.Calibrator.sample_data (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '3b8c85ca1e2cf753cc8c90a6c6992958')) paddle.fluid.contrib.Calibrator.save_int8_model (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.contrib.distributed_sampler (ArgSpec(args=['reader', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', '9a271cd9700deb6d837ed724ba094315')) paddle.fluid.contrib.reader.ctr_reader.ctr_reader (ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2ebf3de2a6ef1af2c3b88d2db7591ab')) -paddle.fluid.contrib.Compressor.__init__ (ArgSpec(args=['self', 'place', 'scope', 'train_program', 'train_reader', 'train_feed_list', 'train_fetch_list', 'eval_program', 'eval_reader', 'eval_feed_list', 'eval_fetch_list', 'teacher_programs', 'checkpoint_path', 'train_optimizer', 'distiller_optimizer'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, [], './checkpoints', None, None)), ('document', '31ae143830c9bf6b43547dd546c5ba80')) +paddle.fluid.contrib.Compressor.__init__ (ArgSpec(args=['self', 'place', 'scope', 'train_program', 'train_reader', 'train_feed_list', 'train_fetch_list', 'eval_program', 'eval_reader', 'eval_feed_list', 'eval_fetch_list', 'teacher_programs', 'checkpoint_path', 'train_optimizer', 'distiller_optimizer', 'search_space'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, [], None, None, None, None)), ('document', 'c195b3bba26169cff9439e8c467557c0')) paddle.fluid.contrib.Compressor.config (ArgSpec(args=['self', 'config_file'], varargs=None, keywords=None, defaults=None), ('document', '780d9c007276ccbb95b292400d7807b0')) paddle.fluid.contrib.Compressor.run (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'c6e43d6a078d307672283c1f36e04fe9')) paddle.fluid.contrib.load_persistables_for_increment (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None), ('document', '2ab36d4f7a564f5f65e455807ad06c67')) @@ -419,15 +428,16 @@ paddle.fluid.contrib.HDFSClient.upload (ArgSpec(args=['self', 'hdfs_path', 'loca paddle.fluid.contrib.multi_download (ArgSpec(args=['client', 'hdfs_path', 'local_path', 'trainer_id', 'trainers', 'multi_processes'], varargs=None, keywords=None, defaults=(5,)), ('document', '100927be598ed8f9eaa1f3ef1b23568a')) paddle.fluid.contrib.multi_upload (ArgSpec(args=['client', 'hdfs_path', 'local_path', 'multi_processes', 'overwrite', 'sync'], varargs=None, keywords=None, defaults=(5, False, True)), ('document', '183f34c83d30dbe16e09e8716c41958a')) paddle.fluid.contrib.extend_with_decoupled_weight_decay (ArgSpec(args=['base_optimizer'], varargs=None, keywords=None, defaults=None), ('document', 'a1095dfd4ec725747f662d69cd7659d4')) -paddle.fluid.contrib.mixed_precision.decorate (ArgSpec(args=['optimizer', 'init_loss_scaling', 'use_dynamic_loss_scaling'], varargs=None, keywords=None, defaults=(1.0, False)), ('document', '67e9bf14f345b38da169beb1ebb276eb')) +paddle.fluid.contrib.mixed_precision.decorate (ArgSpec(args=['optimizer', 'init_loss_scaling', 'incr_every_n_steps', 'decr_every_n_nan_or_inf', 'incr_ratio', 'decr_ratio', 'use_dynamic_loss_scaling'], varargs=None, keywords=None, defaults=(1.0, 1000, 2, 2.0, 0.8, False)), ('document', 'bdb8f9dbb0d94b3957272c53eeee9818')) +paddle.fluid.contrib.fused_elemwise_activation (ArgSpec(args=['x', 'y', 'functor_list', 'axis', 'scale', 'save_intermediate_out'], varargs=None, keywords=None, defaults=(-1, 0.0, True)), ('document', '1c4b247a2858cea8d9d8750693688270')) paddle.fluid.transpiler.DistributeTranspiler.__init__ (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '292ab72977afbe58e6a3bde175452680')) -paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '78f4949aedf317666a89ca74b3748ba8')) -paddle.fluid.transpiler.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'd796fc0c8d51503b556fcf6dc15c4f0c')) -paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '736330e31a7a54abccc0c7fd9119d9ff')) -paddle.fluid.transpiler.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '06ce55338dfe96311ad1078235ab3bf4')) -paddle.fluid.transpiler.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)), ('document', 'eda17d0f1639bc6ca215cecf87f588a4')) -paddle.fluid.transpiler.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ac4114d3df16264f1946deb3a8434a6f')) +paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', 'b1951949c6d21698290aa8ac69afee32')) +paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', 'c89fc350f975ef827f5448d68af388cf')) +paddle.fluid.transpiler.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', '90a40b80e0106f69262cc08b861c3e39')) +paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '0e47f020304e2b824e87ff03475c17cd')) +paddle.fluid.transpiler.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '418c7e8b268e9be4104f2809e654c2f7')) +paddle.fluid.transpiler.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)), ('document', '2348247f684bfd5bb9466470f35be064')) +paddle.fluid.transpiler.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd38c5b8b2b2e0bb19bcf1b581a80a7e4')) paddle.fluid.transpiler.HashName.__init__ (ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.transpiler.HashName.dispatch (ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.transpiler.HashName.reset (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) @@ -438,119 +448,154 @@ paddle.fluid.transpiler.DistributeTranspilerConfig.__init__ paddle.fluid.nets.simple_img_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True)), ('document', '13f01ff80e8dfbd3427d90cf49bc62eb')) paddle.fluid.nets.sequence_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None)), ('document', 'd6a1e527b53f5cc15594fee307dfc5cf')) paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', '6486b2595300fc3305b5a1f0ac363dce')) -paddle.fluid.nets.scaled_dot_product_attention (ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0)), ('document', '921714c9bfb351b41403418265393203')) +paddle.fluid.nets.scaled_dot_product_attention (ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0)), ('document', 'b1a07a0000eb9103e3a143ca8c13de5b')) paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True)), ('document', '5178bc1b4d302192597a5efbae13d902')) paddle.fluid.optimizer.SGDOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.SGDOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.SGDOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.SGDOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.SGDOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.SGDOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.SGDOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.SGDOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.MomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.MomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.MomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.MomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.MomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.MomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.MomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.MomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.AdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name', 'initial_accumulator_value'], varargs=None, keywords=None, defaults=(1e-06, None, None, 0.0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.AdagradOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.AdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.AdagradOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.AdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.AdagradOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.AdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.AdamOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdamOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.AdamOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.AdamOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.AdamOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.AdamOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.AdamOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.AdamOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.AdamaxOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.AdamaxOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.AdamaxOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.AdamaxOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.AdamaxOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.AdamaxOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.AdamaxOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.DecayedAdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.DecayedAdagradOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.DecayedAdagradOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.FtrlOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.FtrlOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.FtrlOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.FtrlOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.FtrlOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.FtrlOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.FtrlOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.FtrlOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.RMSPropOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.RMSPropOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.RMSPropOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.RMSPropOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.RMSPropOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.RMSPropOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.RMSPropOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.AdadeltaOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.AdadeltaOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.AdadeltaOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.AdadeltaOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.AdadeltaOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.AdadeltaOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.AdadeltaOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.ModelAverage.__init__ (ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.ModelAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '46234a5470590feb336346f70a3db715')) +paddle.fluid.optimizer.ModelAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '648010d0ac1fa707dac0b89f74b0e35c')) paddle.fluid.optimizer.ModelAverage.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.ModelAverage.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.ModelAverage.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.ModelAverage.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.ModelAverage.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) -paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '18db9c70be9c4dd466f9844457b21bfe')) +paddle.fluid.optimizer.ModelAverage.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.ModelAverage.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) +paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '5f14ea4adda2791e1c3b37ff327f6a83')) paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.LarsMomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.LarsMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.LarsMomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.LarsMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.LarsMomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.LarsMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) paddle.fluid.optimizer.DGCMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'rampup_begin_step', 'rampup_step', 'sparsity', 'use_nesterov', 'local_grad_clip_norm', 'num_trainers', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1, [0.999], False, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.DGCMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) paddle.fluid.optimizer.DGCMomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.DGCMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) paddle.fluid.optimizer.DGCMomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.optimizer.DGCMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea')) +paddle.fluid.optimizer.DGCMomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.DGCMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) +paddle.fluid.optimizer.LambOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'lamb_weight_decay', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.01, 0.9, 0.999, 1e-06, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.LambOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871')) +paddle.fluid.optimizer.LambOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) +paddle.fluid.optimizer.LambOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f')) +paddle.fluid.optimizer.LambOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.LambOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde')) +paddle.fluid.optimizer.LambOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b')) +paddle.fluid.optimizer.ExponentialMovingAverage.__init__ (ArgSpec(args=['self', 'decay', 'thres_steps', 'name'], varargs=None, keywords=None, defaults=(0.999, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.ExponentialMovingAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '30f494752ac8921dc5835a63637f453a')) +paddle.fluid.optimizer.ExponentialMovingAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '8c8a1791608b02a1ede53d6dd3a4fcec')) +paddle.fluid.optimizer.ExponentialMovingAverage.update (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'ea10f08af6d7aac3b7974aa976e4085f')) +paddle.fluid.optimizer.PipelineOptimizer.__init__ (ArgSpec(args=['self', 'optimizer', 'cut_list', 'place_list', 'concurrency_list', 'queue_size', 'sync_steps', 'start_cpu_core_id'], varargs=None, keywords=None, defaults=(None, None, None, 30, 1, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.PipelineOptimizer.create_vars (ArgSpec(args=['self', 'block', 'main_program'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.PipelineOptimizer.extract_section_ops (ArgSpec(args=['self', 'ops', 'cut_point_name'], varargs=None, keywords=None, defaults=None), ('document', '4a29be77da04b5c30dd7202f44c79b70')) +paddle.fluid.optimizer.PipelineOptimizer.extract_section_opt_ops (ArgSpec(args=['self', 'ops', 'cut_point_name'], varargs=None, keywords=None, defaults=None), ('document', '99e0f641222c1ce4dd0d7194c3b2c653')) +paddle.fluid.optimizer.PipelineOptimizer.find_input_output (ArgSpec(args=['self', 'ops', 'name', 'is_forward'], varargs=None, keywords=None, defaults=(True,)), ('document', '92d77fb262766b352746f09cca81db93')) +paddle.fluid.optimizer.PipelineOptimizer.find_persistable_vars (ArgSpec(args=['self', 'ops', 'whole_parameters'], varargs=None, keywords=None, defaults=None), ('document', '877b7cc290f0647455e5e4409e825923')) +paddle.fluid.optimizer.PipelineOptimizer.find_section_opt (ArgSpec(args=['self', 'ops', 'params'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.PipelineOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.optimizer.PipelineOptimizer.split_program (ArgSpec(args=['self', 'main_program', 'cut_list'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.backward.append_backward (ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '08a5dd9f6f376ff3d55e0b1d92115cbd')) +paddle.fluid.backward.gradients (ArgSpec(args=['targets', 'inputs', 'target_gradients', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'e2097e1e0ed84ae44951437bfe269a1b')) paddle.fluid.regularizer.L1DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.regularizer.L2DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.LoDTensor.__init__ 1. __init__(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None 2. __init__(self: paddle.fluid.core.LoDTensor) -> None -paddle.fluid.LoDTensor.has_valid_recursive_sequence_lengths has_valid_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> bool -paddle.fluid.LoDTensor.lod lod(self: paddle.fluid.core.LoDTensor) -> List[List[int]] -paddle.fluid.LoDTensor.recursive_sequence_lengths recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> List[List[int]] -paddle.fluid.LoDTensor.set 1. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CPUPlace) -> None 2. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CPUPlace) -> None 3. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CPUPlace) -> None 4. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CPUPlace) -> None 5. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CPUPlace) -> None 6. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CPUPlace) -> None 7. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CPUPlace) -> None 8. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CPUPlace) -> None 9. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPlace) -> None 10. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPlace) -> None 11. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPlace) -> None 12. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPlace) -> None 13. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPlace) -> None 14. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPlace) -> None 15. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPlace) -> None 16. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPlace) -> None 17. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPinnedPlace) -> None 18. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPinnedPlace) -> None 19. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPinnedPlace) -> None 20. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPinnedPlace) -> None 21. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPinnedPlace) -> None 22. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPinnedPlace) -> None 23. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPinnedPlace) -> None 24. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPinnedPlace) -> None -paddle.fluid.LoDTensor.set_lod set_lod(self: paddle.fluid.core.LoDTensor, lod: List[List[int]]) -> None -paddle.fluid.LoDTensor.set_recursive_sequence_lengths set_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor, recursive_sequence_lengths: List[List[int]]) -> None -paddle.fluid.LoDTensor.shape shape(self: paddle.fluid.core.Tensor) -> List[int] -paddle.fluid.LoDTensorArray.__init__ __init__(self: paddle.fluid.core.LoDTensorArray) -> None -paddle.fluid.LoDTensorArray.append append(self: paddle.fluid.core.LoDTensorArray, tensor: paddle.fluid.core.LoDTensor) -> None -paddle.fluid.CPUPlace.__init__ __init__(self: paddle.fluid.core.CPUPlace) -> None -paddle.fluid.CUDAPlace.__init__ __init__(self: paddle.fluid.core.CUDAPlace, arg0: int) -> None -paddle.fluid.CUDAPinnedPlace.__init__ __init__(self: paddle.fluid.core.CUDAPinnedPlace) -> None +paddle.fluid.LoDTensor.__init__ 1. __init__(self: paddle.fluid.core_avx.LoDTensor, arg0: List[List[int]]) -> None 2. __init__(self: paddle.fluid.core_avx.LoDTensor) -> None +paddle.fluid.LoDTensor.has_valid_recursive_sequence_lengths has_valid_recursive_sequence_lengths(self: paddle.fluid.core_avx.LoDTensor) -> bool +paddle.fluid.LoDTensor.lod lod(self: paddle.fluid.core_avx.LoDTensor) -> List[List[int]] +paddle.fluid.LoDTensor.recursive_sequence_lengths recursive_sequence_lengths(self: paddle.fluid.core_avx.LoDTensor) -> List[List[int]] +paddle.fluid.LoDTensor.set_lod set_lod(self: paddle.fluid.core_avx.LoDTensor, lod: List[List[int]]) -> None +paddle.fluid.LoDTensor.set_recursive_sequence_lengths set_recursive_sequence_lengths(self: paddle.fluid.core_avx.LoDTensor, recursive_sequence_lengths: List[List[int]]) -> None +paddle.fluid.LoDTensor.shape shape(self: paddle.fluid.core_avx.Tensor) -> List[int] +paddle.fluid.LoDTensorArray.__init__ __init__(self: paddle.fluid.core_avx.LoDTensorArray) -> None +paddle.fluid.LoDTensorArray.append append(self: paddle.fluid.core_avx.LoDTensorArray, tensor: paddle.fluid.core_avx.LoDTensor) -> None +paddle.fluid.CPUPlace.__init__ __init__(self: paddle.fluid.core_avx.CPUPlace) -> None +paddle.fluid.CUDAPlace.__init__ __init__(self: paddle.fluid.core_avx.CUDAPlace, arg0: int) -> None +paddle.fluid.CUDAPinnedPlace.__init__ __init__(self: paddle.fluid.core_avx.CUDAPinnedPlace) -> None paddle.fluid.ParamAttr.__init__ (ArgSpec(args=['self', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, 1.0, None, True, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.WeightNormParamAttr.__init__ (ArgSpec(args=['self', 'dim', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, None, 1.0, None, True, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.DataFeeder.__init__ (ArgSpec(args=['self', 'feed_list', 'place', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.DataFeeder.decorate_reader (ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)), ('document', 'f8f3df23c5633c614db781a91b81fb62')) -paddle.fluid.DataFeeder.feed (ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None), ('document', '459e316301279dfd82001b46f0b8ffca')) -paddle.fluid.DataFeeder.feed_parallel (ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,)), ('document', '543863d1f9d4853758adb613b8659e85')) +paddle.fluid.DataFeeder.decorate_reader (ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)), ('document', 'be47d7e07824b4281da77472846955ac')) +paddle.fluid.DataFeeder.feed (ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None), ('document', 'ce65fe1d81dcd7067d5092a5667f35cc')) +paddle.fluid.DataFeeder.feed_parallel (ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c312743c910dda1c3a9c6637ac30187f')) paddle.fluid.clip.ErrorClipByValue.__init__ (ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.clip.GradientClipByValue.__init__ (ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.clip.GradientClipByNorm.__init__ (ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.clip.GradientClipByGlobalNorm.__init__ (ArgSpec(args=['self', 'clip_norm', 'group_name'], varargs=None, keywords=None, defaults=('default_group',)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph_grad_clip.GradClipByValue.__init__ (ArgSpec(args=['self', 'min_value', 'max_value'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph_grad_clip.GradClipByNorm.__init__ (ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph_grad_clip.GradClipByGlobalNorm.__init__ (ArgSpec(args=['self', 'max_global_norm'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.profiler.cuda_profiler (ArgSpec(args=['output_file', 'output_mode', 'config'], varargs=None, keywords=None, defaults=(None, None)), ('document', '49f5db5da13cfd8c069754dd11be3901')) paddle.fluid.profiler.reset_profiler (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'd33483b1781e47c4c5d5fefa7b7debcb')) paddle.fluid.profiler.profiler (ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', 'd8db46bf9a579bec476d09dea80eb23d')) paddle.fluid.profiler.start_profiler (ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None), ('document', '88da8fb6dbebaee2f7520188a09574f9')) paddle.fluid.profiler.stop_profiler (ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', 'a7500e39dd033f1e64f562e909333a8a')) -paddle.fluid.unique_name.generate (ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.unique_name.switch (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) -paddle.fluid.unique_name.guard (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.unique_name.generate (ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None), ('document', '4d68cde4c4df8f1b8018620b4dc19b42')) +paddle.fluid.unique_name.switch (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', '695a6e91afbcdbafac69a069038811be')) +paddle.fluid.unique_name.guard (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ead717d6d440a1eb11971695cd1727f4')) paddle.fluid.recordio_writer.convert_reader_to_recordio_file (ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None)), ('document', '65c7523e86f0c50bb729b01667f36310')) paddle.fluid.recordio_writer.convert_reader_to_recordio_files (ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None)), ('document', 'bc643f0f5f1b9db57ff0d8a57d379bd7')) -paddle.fluid.Scope Scope() -> paddle.fluid.core._Scope +paddle.fluid.Scope Scope() -> paddle.fluid.core_avx._Scope paddle.fluid.install_check.run_check (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '66b7c84a17ed32fec2df9628367be2b9')) paddle.reader.cache (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', '1676886070eb607cb608f7ba47be0d3c')) paddle.reader.map_readers (ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None), ('document', '77cbadb09df588e21e5cc0819b69c87d')) diff --git a/paddle/fluid/CMakeLists.txt b/paddle/fluid/CMakeLists.txt index c212d579921c24a714c1e3ee6e678a41dca604f4..595454e90b9cd713fd2baed24538cf5fbc93934a 100644 --- a/paddle/fluid/CMakeLists.txt +++ b/paddle/fluid/CMakeLists.txt @@ -1,7 +1,3 @@ -if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK) # for mobile - add_subdirectory(lite) - return() -endif() add_subdirectory(memory) add_subdirectory(platform) add_subdirectory(framework) @@ -10,8 +6,7 @@ add_subdirectory(operators) add_subdirectory(string) add_subdirectory(recordio) add_subdirectory(pybind) -add_subdirectory(train) + # NOTE: please add subdirectory inference at last. add_subdirectory(inference) - -add_subdirectory(lite) +add_subdirectory(train) diff --git a/paddle/fluid/framework/CMakeLists.txt b/paddle/fluid/framework/CMakeLists.txt index ce33a70c54978c9ba907d33395afb4dacde4ca50..65367a21209a9e2a131e92ef1416bcb351470e75 100644 --- a/paddle/fluid/framework/CMakeLists.txt +++ b/paddle/fluid/framework/CMakeLists.txt @@ -29,7 +29,8 @@ add_subdirectory(io) proto_library(framework_proto SRCS framework.proto) proto_library(data_feed_proto SRCS data_feed.proto) proto_library(async_executor_proto SRCS data_feed.proto) -proto_library(trainer_desc_proto SRCS trainer_desc.proto data_feed.proto) +proto_library(trainer_desc_proto SRCS trainer_desc.proto DEPS framework_proto + data_feed_proto) cc_library(ddim SRCS ddim.cc DEPS eigen3 boost enforce) cc_test(ddim_test SRCS ddim_test.cc DEPS ddim) @@ -124,7 +125,7 @@ cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_co cc_library(transfer_scope_cache SRCS transfer_scope_cache.cc DEPS scope framework_proto device_context) cc_library(op_kernel_type SRCS op_kernel_type.cc DEPS device_context place) cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog - shape_inference data_transform lod_tensor profiler transfer_scope_cache op_kernel_type data_feed_proto) + shape_inference data_transform lod_tensor profiler transfer_scope_cache op_kernel_type) cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry device_context) @@ -173,20 +174,20 @@ endif() cc_library(executor_gc_helper SRCS executor_gc_helper.cc DEPS scope proto_desc operator garbage_collector) if(WITH_DISTRIBUTE) - cc_library(executor SRCS executor.cc multi_trainer.cc dataset_factory.cc + cc_library(executor SRCS executor.cc multi_trainer.cc pipeline_trainer.cc dataset_factory.cc dist_multi_trainer.cc trainer_factory.cc trainer.cc data_feed_factory.cc data_feed.cc device_worker.cc hogwild_worker.cc downpour_worker.cc - pull_dense_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry + pull_dense_worker.cc section_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry device_context scope framework_proto trainer_desc_proto glog fs shell fleet_wrapper lodtensor_printer lod_rank_table feed_fetch_method sendrecvop_rpc ${GLOB_DISTRIBUTE_DEPS} graph_to_program_pass variable_helper data_feed_proto ${NGRAPH_EXE_DEPS} timer) set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor") set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) else() - cc_library(executor SRCS executor.cc multi_trainer.cc dataset_factory.cc + cc_library(executor SRCS executor.cc multi_trainer.cc pipeline_trainer.cc dataset_factory.cc dist_multi_trainer.cc trainer_factory.cc trainer.cc data_feed_factory.cc data_feed.cc device_worker.cc hogwild_worker.cc downpour_worker.cc - pull_dense_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry + pull_dense_worker.cc section_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry device_context scope framework_proto data_feed_proto trainer_desc_proto glog lod_rank_table fs shell fleet_wrapper lodtensor_printer feed_fetch_method graph_to_program_pass variable_helper ${NGRAPH_EXE_DEPS} timer data_feed_proto) @@ -201,10 +202,10 @@ cc_library(parallel_executor SRCS parallel_executor.cc DEPS fast_threaded_ssa_graph_executor variable_helper) cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc - executor_thread_worker.cc multi_trainer.cc dist_multi_trainer.cc + executor_thread_worker.cc multi_trainer.cc dist_multi_trainer.cc pipeline_trainer.cc trainer_factory.cc trainer.cc device_worker.cc hogwild_worker.cc - downpour_worker.cc pull_dense_worker.cc device_worker_factory.cc - data_set.cc dataset_factory.cc + downpour_worker.cc pull_dense_worker.cc section_worker.cc + device_worker_factory.cc data_set.cc dataset_factory.cc DEPS op_registry device_context scope framework_proto trainer_desc_proto glog lod_rank_table fleet_wrapper lodtensor_printer feed_fetch_method graph_to_program_pass data_feed_proto @@ -225,6 +226,8 @@ cc_test(cow_ptr_tests SRCS details/cow_ptr_test.cc) cc_test(tuple_test SRCS tuple_test.cc ) +cc_test(inlined_vector_test SRCS inlined_vector_test.cc) + if (NOT WIN32) cc_test(rw_lock_test SRCS rw_lock_test.cc) endif (NOT WIN32) diff --git a/paddle/fluid/framework/async_executor.cc b/paddle/fluid/framework/async_executor.cc index 89153d82d078b53d8d5582f0a38d3dafe21cc7eb..7eb80a4617ae547751a77449977ffeb245226bb0 100644 --- a/paddle/fluid/framework/async_executor.cc +++ b/paddle/fluid/framework/async_executor.cc @@ -85,8 +85,9 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program, } DataFeedDesc data_feed_desc; - google::protobuf::TextFormat::ParseFromString(data_feed_desc_str, - &data_feed_desc); + bool success = data_feed_desc.ParseFromString(data_feed_desc_str); + PADDLE_ENFORCE(success, "Fail to parse DataFeedDesc from string:\n%s", + data_feed_desc_str.c_str()); actual_thread_num_ = thread_num; int file_cnt = filelist.size(); diff --git a/paddle/fluid/framework/blocking_queue.h b/paddle/fluid/framework/blocking_queue.h index cc5b4e8c4b8e114668f472ea2af9de96835720d0..4f35da402f3ec2b0616c29085d01e8b7f3d0d472 100644 --- a/paddle/fluid/framework/blocking_queue.h +++ b/paddle/fluid/framework/blocking_queue.h @@ -95,6 +95,11 @@ class BlockingQueue { return q_.size(); } + void Clear() { + std::lock_guard lock(mutex_); + std::deque().swap(q_); + } + private: std::mutex mutex_; std::condition_variable cv_; diff --git a/paddle/fluid/framework/data_feed.cc b/paddle/fluid/framework/data_feed.cc index 02e467e853e9c3e7a4d581043e6a8f7b70519521..e89f3f1a4e06f1ea94a2050f03ebc6c58c591625 100644 --- a/paddle/fluid/framework/data_feed.cc +++ b/paddle/fluid/framework/data_feed.cc @@ -20,6 +20,9 @@ limitations under the License. */ #include "paddle/fluid/framework/data_feed.h" #ifdef _LINUX #include +#include +#include +#include #endif #include #include "gflags/gflags.h" @@ -87,6 +90,13 @@ void DataFeed::CheckStart() { PADDLE_ENFORCE(finish_start_, "Datafeed has not started running yet."); } +void DataFeed::AssignFeedVar(const Scope& scope) { + CheckInit(); + for (size_t i = 0; i < use_slots_.size(); ++i) { + feed_vec_[i] = scope.FindVar(use_slots_[i])->GetMutable(); + } +} + template void PrivateQueueDataFeed::SetQueueSize(int queue_size) { PADDLE_ENFORCE(queue_size > 0, "Illegal queue size: %d.", queue_size); @@ -158,6 +168,7 @@ InMemoryDataFeed::InMemoryDataFeed() { mutex_for_update_memory_data_ = nullptr; this->file_idx_ = nullptr; this->mutex_for_pick_file_ = nullptr; + fleet_send_sleep_seconds_ = 2; } template @@ -366,7 +377,7 @@ void InMemoryDataFeed::GlobalShuffle() { auto fleet_ptr = FleetWrapper::GetInstance(); std::vector> send_vec(trainer_num_); std::vector send_index(trainer_num_); - uint64_t reserve_len = fleet_send_batch_size_ / trainer_num_; + uint64_t reserve_len = fleet_send_batch_size_ / trainer_num_ + 1; for (auto& vec : send_vec) { vec.reserve(reserve_len); } @@ -377,46 +388,33 @@ void InMemoryDataFeed::GlobalShuffle() { auto interval = GetMemoryDataInterval(); VLOG(3) << "global shuffle data from [" << interval.first << ", " << interval.second << "), thread_id=" << thread_id_; - for (int64_t i = interval.first; i < interval.second; ++i) { - // if get ins id, can also use hash - // std::string ins_id = memory_data_[i].ins_id; - int64_t random_num = rand_r(&rand_seed); - int64_t node_id = random_num % trainer_num_; - send_vec[node_id].push_back(&((*memory_data_)[i])); - if (i % fleet_send_batch_size_ == 0 && i != 0) { - // shuffle the sequence of sending to avoid network timeout error - std::random_shuffle(send_index.begin(), send_index.end()); - for (int index = 0; index < send_index.size(); ++index) { - int j = send_index[index]; - std::string send_str; - SerializeIns(send_vec[j], &send_str); - VLOG(3) << "send str_length=" << send_str.length() - << ", ins num=" << send_vec[j].size() << " to node_id=" << j - << ", thread_id=" << thread_id_; - auto ret = fleet_ptr->SendClientToClientMsg(0, j, send_str); - VLOG(3) << "end send, thread_id=" << thread_id_; - send_vec[j].clear(); - total_status.push_back(std::move(ret)); - } + + for (int64_t i = interval.first; i < interval.second; + i += fleet_send_batch_size_) { + for (int64_t j = 0; j < fleet_send_batch_size_ && i + j < interval.second; + ++j) { + int64_t random_num = fleet_ptr->LocalRandomEngine()(); + int64_t node_id = random_num % trainer_num_; + send_vec[node_id].push_back(&((*memory_data_)[i + j])); } - } - // shuffle the sequence of sending to avoid network timeout error - std::random_shuffle(send_index.begin(), send_index.end()); - for (int index = 0; index < send_index.size(); ++index) { - int j = send_index[index]; - if (send_vec[j].size() != 0) { + total_status.clear(); + std::shuffle(send_index.begin(), send_index.end(), + fleet_ptr->LocalRandomEngine()); + for (int index = 0; index < send_index.size(); ++index) { + int j = send_index[index]; + if (send_vec[j].size() == 0) { + continue; + } std::string send_str; SerializeIns(send_vec[j], &send_str); - VLOG(3) << "send str_length=" << send_str.length() << " to node_id=" << j - << ", thread_id=" << thread_id_; auto ret = fleet_ptr->SendClientToClientMsg(0, j, send_str); - VLOG(3) << "end send, thread_id=" << thread_id_; total_status.push_back(std::move(ret)); + send_vec[j].clear(); } - std::vector().swap(send_vec[j]); - } - for (auto& t : total_status) { - t.wait(); + for (auto& t : total_status) { + t.wait(); + } + sleep(fleet_send_sleep_seconds_); } VLOG(3) << "GlobalShuffle() end, thread_id=" << thread_id_; #endif @@ -436,6 +434,24 @@ std::pair InMemoryDataFeed::GetMemoryDataInterval() { return std::make_pair(start, end); } +template +int64_t InMemoryDataFeed::GetChannelDataSize() { + if (cur_channel_ == 0) { + return shuffled_ins_->Size(); + } else { + return shuffled_ins_out_->Size(); + } +} + +template +void InMemoryDataFeed::ReleaseChannelData() { + if (cur_channel_ == 0) { + shuffled_ins_->Clear(); + } else { + shuffled_ins_out_->Clear(); + } +} + // explicit instantiation template class InMemoryDataFeed>; @@ -471,17 +487,17 @@ void MultiSlotDataFeed::Init( use_slots_is_dense_.push_back(slot.is_dense()); std::vector local_shape; if (slot.is_dense()) { - for (size_t i = 0; i < slot.shape_size(); ++i) { - if (slot.shape(i) > 0) { - total_dims_without_inductive_[i] *= slot.shape(i); + for (size_t j = 0; j < slot.shape_size(); ++j) { + if (slot.shape(j) > 0) { + total_dims_without_inductive_[i] *= slot.shape(j); } - if (slot.shape(i) == -1) { - inductive_shape_index_[i] = i; + if (slot.shape(j) == -1) { + inductive_shape_index_[i] = j; } } } - for (size_t i = 0; i < slot.shape_size(); ++i) { - local_shape.push_back(slot.shape(i)); + for (size_t j = 0; j < slot.shape_size(); ++j) { + local_shape.push_back(slot.shape(j)); } use_slots_shape_.push_back(local_shape); } @@ -805,22 +821,24 @@ void MultiSlotInMemoryDataFeed::Init( all_slots_[i] = slot.name(); all_slots_type_[i] = slot.type(); use_slots_index_[i] = slot.is_used() ? use_slots_.size() : -1; + total_dims_without_inductive_[i] = 1; + inductive_shape_index_[i] = -1; if (slot.is_used()) { use_slots_.push_back(all_slots_[i]); use_slots_is_dense_.push_back(slot.is_dense()); std::vector local_shape; if (slot.is_dense()) { - for (size_t i = 0; i < slot.shape_size(); ++i) { - if (slot.shape(i) > 0) { - total_dims_without_inductive_[i] *= slot.shape(i); + for (size_t j = 0; j < slot.shape_size(); ++j) { + if (slot.shape(j) > 0) { + total_dims_without_inductive_[i] *= slot.shape(j); } - if (slot.shape(i) == -1) { - inductive_shape_index_[i] = i; + if (slot.shape(j) == -1) { + inductive_shape_index_[i] = j; } } } - for (size_t i = 0; i < slot.shape_size(); ++i) { - local_shape.push_back(slot.shape(i)); + for (size_t j = 0; j < slot.shape_size(); ++j) { + local_shape.push_back(slot.shape(j)); } use_slots_shape_.push_back(local_shape); } @@ -1001,5 +1019,205 @@ void MultiSlotInMemoryDataFeed::DeserializeIns( fleet_ptr->Deserialize(ins, str); } +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +template +void PrivateInstantDataFeed::PutToFeedVec() { + for (size_t i = 0; i < use_slots_.size(); ++i) { + const auto& type = ins_vec_[i].GetType(); + const auto& offset = ins_vec_[i].GetOffset(); + int total_instance = static_cast(offset.back()); + + if (type[0] == 'f') { // float + const auto& feasign = ins_vec_[i].GetFloatData(); + float* tensor_ptr = feed_vec_[i]->mutable_data( + {total_instance, 1}, platform::CPUPlace()); + memcpy(tensor_ptr, &feasign[0], total_instance * sizeof(float)); + } else if (type[0] == 'u') { // uint64 + // no uint64_t type in paddlepaddle + const auto& feasign = ins_vec_[i].GetUint64Data(); + int64_t* tensor_ptr = feed_vec_[i]->mutable_data( + {total_instance, 1}, platform::CPUPlace()); + memcpy(tensor_ptr, &feasign[0], total_instance * sizeof(int64_t)); + } + + LoD data_lod{offset}; + feed_vec_[i]->set_lod(data_lod); + if (use_slots_is_dense_[i]) { + int64_t total_dims = 1; + for (const auto e : use_slots_shape_[i]) { + total_dims *= e; + } + PADDLE_ENFORCE( + total_dims == total_instance, + "The actual data size of slot[%s] doesn't match its declaration", + use_slots_[i].c_str()); + feed_vec_[i]->Resize(framework::make_ddim(use_slots_shape_[i])); + } + } +} + +template +int PrivateInstantDataFeed::Next() { + if (ParseOneMiniBatch()) { + PutToFeedVec(); + return ins_vec_[0].GetBatchSize(); + } + Postprocess(); + + std::string filename; + if (!PickOneFile(&filename)) { + return -1; + } + if (!Preprocess(filename)) { + return -1; + } + + PADDLE_ENFORCE(true == ParseOneMiniBatch(), "Fail to parse mini-batch data"); + PutToFeedVec(); + return ins_vec_[0].GetBatchSize(); +} + +template +void PrivateInstantDataFeed::Init(const DataFeedDesc& data_feed_desc) { + finish_init_ = false; + finish_set_filelist_ = false; + finish_start_ = false; + + PADDLE_ENFORCE(data_feed_desc.has_multi_slot_desc(), + "Multi_slot_desc has not been set."); + paddle::framework::MultiSlotDesc multi_slot_desc = + data_feed_desc.multi_slot_desc(); + SetBatchSize(data_feed_desc.batch_size()); + size_t all_slot_num = multi_slot_desc.slots_size(); + all_slots_.resize(all_slot_num); + all_slots_type_.resize(all_slot_num); + use_slots_index_.resize(all_slot_num); + multi_inductive_shape_index_.resize(all_slot_num); + use_slots_.clear(); + use_slots_is_dense_.clear(); + for (size_t i = 0; i < all_slot_num; ++i) { + const auto& slot = multi_slot_desc.slots(i); + all_slots_[i] = slot.name(); + all_slots_type_[i] = slot.type(); + use_slots_index_[i] = slot.is_used() ? use_slots_.size() : -1; + if (slot.is_used()) { + use_slots_.push_back(all_slots_[i]); + use_slots_is_dense_.push_back(slot.is_dense()); + std::vector local_shape; + if (slot.is_dense()) { + for (size_t j = 0; j < slot.shape_size(); ++j) { + if (slot.shape(j) == -1) { + multi_inductive_shape_index_[i].push_back(j); + } + } + } + for (size_t j = 0; j < slot.shape_size(); ++j) { + local_shape.push_back(slot.shape(j)); + } + use_slots_shape_.push_back(local_shape); + } + } + feed_vec_.resize(use_slots_.size()); + ins_vec_.resize(use_slots_.size()); + + finish_init_ = true; +} + +template class PrivateInstantDataFeed>; + +bool MultiSlotFileInstantDataFeed::Preprocess(const std::string& filename) { + fd_ = open(filename.c_str(), O_RDONLY); + PADDLE_ENFORCE(fd_ != -1, "Fail to open file: %s", filename.c_str()); + + struct stat sb; + fstat(fd_, &sb); + end_ = static_cast(sb.st_size); + + buffer_ = + reinterpret_cast(mmap(NULL, end_, PROT_READ, MAP_PRIVATE, fd_, 0)); + PADDLE_ENFORCE(buffer_ != MAP_FAILED, strerror(errno)); + + offset_ = 0; + return true; +} + +bool MultiSlotFileInstantDataFeed::Postprocess() { + if (buffer_ != nullptr) { + munmap(buffer_, end_); + buffer_ = nullptr; + } + if (fd_ != -1) { + close(fd_); + fd_ = -1; + end_ = 0; + offset_ = 0; + } + return true; +} + +bool MultiSlotFileInstantDataFeed::ParseOneMiniBatch() { + if (offset_ == end_) { + return false; + } + + batch_size_ = 0; + while (batch_size_ < default_batch_size_ && offset_ < end_) { + for (size_t i = 0; i < use_slots_index_.size(); ++i) { + int idx = use_slots_index_[i]; + char type = all_slots_type_[i][0]; + + uint16_t num = *reinterpret_cast(buffer_ + offset_); + PADDLE_ENFORCE( + num, + "The number of ids can not be zero, you need padding " + "it in data generator; or if there is something wrong with " + "the data, please check if the data contains unresolvable " + "characters."); + offset_ += sizeof(uint16_t); + + if (idx != -1) { + int inductive_size = multi_inductive_shape_index_[i].size(); + if (UNLIKELY(batch_size_ == 0)) { + ins_vec_[idx].Init(all_slots_type_[i], default_batch_size_ * num); + ins_vec_[idx].InitOffset(default_batch_size_); + uint64_t* inductive_shape = + reinterpret_cast(buffer_ + offset_); + for (int inductive_id = 0; inductive_id < inductive_size; + ++inductive_id) { + use_slots_shape_[i][multi_inductive_shape_index_[i][inductive_id]] = + static_cast(*(inductive_shape + inductive_id)); + } + } + num -= inductive_size; + offset_ += sizeof(uint64_t) * inductive_size; + + if (type == 'f') { + ins_vec_[idx].AppendValues( + reinterpret_cast(buffer_ + offset_), num); + offset_ += num * sizeof(float); + } else if (type == 'u') { + ins_vec_[idx].AppendValues( + reinterpret_cast(buffer_ + offset_), num); + offset_ += num * sizeof(uint64_t); + } + } else { + if (type == 'f') { + offset_ += num * sizeof(float); + } else if (type == 'u') { + offset_ += num * sizeof(uint64_t); + } + } + } + ++batch_size_; + // OPTIMIZE: It is better to insert check codes between instances for format + // checking + } + + PADDLE_ENFORCE(batch_size_ == default_batch_size_ || offset_ == end_, + "offset_ != end_"); + return true; +} +#endif + } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/data_feed.h b/paddle/fluid/framework/data_feed.h index c141059a6d6b3420f02c1b6090cf67db7b7b4da8..7fea85601c4e3f884c0fc26b5d5197d3b09cdc96 100644 --- a/paddle/fluid/framework/data_feed.h +++ b/paddle/fluid/framework/data_feed.h @@ -59,7 +59,7 @@ class DataFeed { file_idx_ = nullptr; } virtual ~DataFeed() {} - virtual void Init(const paddle::framework::DataFeedDesc& data_feed_desc) = 0; + virtual void Init(const DataFeedDesc& data_feed_desc) = 0; virtual bool CheckFile(const char* filename) { PADDLE_THROW("This function(CheckFile) is not implemented."); } @@ -84,6 +84,9 @@ class DataFeed { // This function is used for binding feed_vec memory virtual void AddFeedVar(Variable* var, const std::string& name); + // This function is used for binding feed_vec memory in a given scope + virtual void AssignFeedVar(const Scope& scope); + // This function will do nothing at default virtual void SetMemoryData(void* memory_data) {} // This function will do nothing at default @@ -115,6 +118,9 @@ class DataFeed { virtual void FillChannelToMemoryData() {} // This function will do nothing at default virtual void PutInsToChannel(const std::string& ins_str) {} + virtual int64_t GetChannelDataSize() { return 0; } + // This function will do nothing at default + virtual void ReleaseChannelData() {} protected: // The following three functions are used to check if it is executed in this @@ -145,6 +151,8 @@ class DataFeed { std::vector> use_slots_shape_; std::vector inductive_shape_index_; std::vector total_dims_without_inductive_; + // For the inductive shape passed within data + std::vector> multi_inductive_shape_index_; std::vector use_slots_index_; // -1: not used; >=0: the index of use_slots_ @@ -170,7 +178,6 @@ class PrivateQueueDataFeed : public DataFeed { public: PrivateQueueDataFeed() {} virtual ~PrivateQueueDataFeed() {} - virtual void Init(const paddle::framework::DataFeedDesc& data_feed_desc) = 0; virtual bool Start(); virtual int Next(); @@ -209,7 +216,7 @@ class InMemoryDataFeed : public PrivateQueueDataFeed { public: InMemoryDataFeed(); virtual ~InMemoryDataFeed() {} - virtual void Init(const paddle::framework::DataFeedDesc& data_feed_desc) = 0; + virtual void Init(const DataFeedDesc& data_feed_desc) = 0; virtual bool Start(); virtual int Next(); virtual void SetMemoryData(void* memory_data); @@ -224,6 +231,8 @@ class InMemoryDataFeed : public PrivateQueueDataFeed { virtual void LoadIntoMemory(); virtual void LocalShuffle(); virtual void GlobalShuffle(); + virtual int64_t GetChannelDataSize(); + virtual void ReleaseChannelData(); protected: virtual void AddInstanceToInsVec(T* vec_ins, const T& instance, @@ -248,6 +257,9 @@ class InMemoryDataFeed : public PrivateQueueDataFeed { std::shared_ptr> shuffled_ins_; std::shared_ptr> shuffled_ins_out_; int64_t fleet_send_batch_size_; + // sleep after send is to slow down sending data, but it's trick, + // should be removed later. + int64_t fleet_send_sleep_seconds_; }; // This class define the data type of instance(ins_vec) in MultiSlotDataFeed @@ -255,16 +267,25 @@ class MultiSlotType { public: MultiSlotType() {} ~MultiSlotType() {} - void Init(const std::string& type) { + void Init(const std::string& type, size_t reserved_size = 0) { CheckType(type); if (type_[0] == 'f') { float_feasign_.clear(); + if (reserved_size) { + float_feasign_.reserve(reserved_size); + } } else if (type_[0] == 'u') { uint64_feasign_.clear(); + if (reserved_size) { + uint64_feasign_.reserve(reserved_size); + } } type_ = type; } - void InitOffset() { + void InitOffset(size_t max_batch_size = 0) { + if (max_batch_size > 0) { + offset_.reserve(max_batch_size + 1); + } offset_.resize(1); // LoDTensor' lod is counted from 0, the size of lod // is one size larger than the size of data. @@ -280,6 +301,16 @@ class MultiSlotType { CheckUint64(); uint64_feasign_.push_back(v); } + void CopyValues(const float* input, size_t size) { + CheckFloat(); + float_feasign_.resize(size); + memcpy(float_feasign_.data(), input, size * sizeof(float)); + } + void CopyValues(const uint64_t* input, size_t size) { + CheckUint64(); + uint64_feasign_.resize(size); + memcpy(uint64_feasign_.data(), input, size * sizeof(uint64_t)); + } void AddIns(const MultiSlotType& ins) { if (ins.GetType()[0] == 'f') { // float CheckFloat(); @@ -293,11 +324,22 @@ class MultiSlotType { uint64_feasign_.insert(uint64_feasign_.end(), vec.begin(), vec.end()); } } + void AppendValues(const uint64_t* input, size_t size) { + CheckUint64(); + offset_.push_back(offset_.back() + size); + uint64_feasign_.insert(uint64_feasign_.end(), input, input + size); + } + void AppendValues(const float* input, size_t size) { + CheckFloat(); + offset_.push_back(offset_.back() + size); + float_feasign_.insert(float_feasign_.end(), input, input + size); + } const std::vector& GetFloatData() const { return float_feasign_; } std::vector& MutableFloatData() { return float_feasign_; } const std::vector& GetUint64Data() const { return uint64_feasign_; } std::vector& MutableUint64Data() { return uint64_feasign_; } const std::string& GetType() const { return type_; } + size_t GetBatchSize() { return offset_.size() - 1; } std::string& MutableType() { return type_; } std::string DebugString() { @@ -347,7 +389,7 @@ class MultiSlotDataFeed public: MultiSlotDataFeed() {} virtual ~MultiSlotDataFeed() {} - virtual void Init(const paddle::framework::DataFeedDesc& data_feed_desc); + virtual void Init(const DataFeedDesc& data_feed_desc); virtual bool CheckFile(const char* filename); // virtual void ReadThread(); @@ -366,7 +408,7 @@ class MultiSlotInMemoryDataFeed public: MultiSlotInMemoryDataFeed() {} virtual ~MultiSlotInMemoryDataFeed() {} - virtual void Init(const paddle::framework::DataFeedDesc& data_feed_desc); + virtual void Init(const DataFeedDesc& data_feed_desc); protected: virtual void AddInstanceToInsVec(std::vector* vec_ins, @@ -381,5 +423,54 @@ class MultiSlotInMemoryDataFeed const std::string& str); }; +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +template +class PrivateInstantDataFeed : public DataFeed { + public: + PrivateInstantDataFeed() {} + virtual ~PrivateInstantDataFeed() {} + void Init(const DataFeedDesc& data_feed_desc) override; + bool Start() override { return true; } + int Next() override; + + protected: + // The batched data buffer + std::vector ins_vec_; + + // This function is used to preprocess with a given filename, e.g. open it or + // mmap + virtual bool Preprocess(const std::string& filename) = 0; + + // This function is used to postprocess system resource such as closing file + // NOTICE: Ensure that it is safe to call before Preprocess + virtual bool Postprocess() = 0; + + // The reading and parsing method. + virtual bool ParseOneMiniBatch() = 0; + + // This function is used to put ins_vec to feed_vec + virtual void PutToFeedVec(); +}; + +class MultiSlotFileInstantDataFeed + : public PrivateInstantDataFeed> { + public: + MultiSlotFileInstantDataFeed() {} + virtual ~MultiSlotFileInstantDataFeed() {} + + protected: + int fd_{-1}; + char* buffer_{nullptr}; + size_t end_{0}; + size_t offset_{0}; + + bool Preprocess(const std::string& filename) override; + + bool Postprocess() override; + + bool ParseOneMiniBatch() override; +}; +#endif + } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/data_feed_factory.cc b/paddle/fluid/framework/data_feed_factory.cc index 201d6c0d0b96469afbee1c3262e549d9d4e512dd..ec1acad99bc9b5e96fbe2433ba2bb9a62fb36966 100644 --- a/paddle/fluid/framework/data_feed_factory.cc +++ b/paddle/fluid/framework/data_feed_factory.cc @@ -64,5 +64,8 @@ std::shared_ptr DataFeedFactory::CreateDataFeed( REGISTER_DATAFEED_CLASS(MultiSlotDataFeed); REGISTER_DATAFEED_CLASS(MultiSlotInMemoryDataFeed); +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +REGISTER_DATAFEED_CLASS(MultiSlotFileInstantDataFeed); +#endif } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/data_layout_transform.cc b/paddle/fluid/framework/data_layout_transform.cc index 72c50518af08b9c1b2f97e6864e5836e806c77fc..bbcd34260e3645e76352ef84bb1d9ae7882a65bb 100644 --- a/paddle/fluid/framework/data_layout_transform.cc +++ b/paddle/fluid/framework/data_layout_transform.cc @@ -13,11 +13,13 @@ // limitations under the License. #include "paddle/fluid/framework/data_layout_transform.h" +#include #include #include "paddle/fluid/operators/math/math_function.h" #ifdef PADDLE_WITH_MKLDNN #include "paddle/fluid/platform/mkldnn_helper.h" +#include "paddle/fluid/platform/mkldnn_reuse.h" #endif namespace paddle { @@ -145,7 +147,6 @@ void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var, memory::data_type in_type = ToMKLDNNDataType(in.type()); PADDLE_ENFORCE(in_type != memory::data_type::data_undef, "Input tensor type is not supported: %s", in.type()); - memory::data_type out_type = in_type; auto in_format = platform::MKLDNNFormatForSize(in_tz.size(), in.format()); auto out_format = @@ -156,14 +157,21 @@ void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var, if (in_format != out_format) { void* in_data = GetDataFromTensor(in, in_type); - auto out_data = out->mutable_data(expected_kernel_type.place_, in.type()); + const std::string key = platform::ReorderMKLDNNHandler::GetHash( + in_tz, in_format, out_format, std::to_string(in_type)); - auto in_memory = - memory({{{in_tz}, in_type, in_format}, cpu_engine}, in_data); - auto out_memory = - memory({{{out_tz}, out_type, out_format}, cpu_engine}, out_data); + platform::ReorderMKLDNNHandler handler(in_tz, in.type(), in_type, *dev_ctx, + cpu_engine, key); - platform::Reorder(in_memory, out_memory); + auto reorder_src_memory_p = handler.AcquireSrcMemory(in_format, in_data); + auto reorder_dst_memory_p = + handler.AcquireDstMemory(out, out_format, expected_kernel_type.place_); + auto reorder_p = + handler.AcquireReorder(reorder_dst_memory_p, reorder_src_memory_p); + + std::vector pipeline; + pipeline.push_back(*reorder_p); + mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); } else { out->ShareDataWith(in); } diff --git a/paddle/fluid/framework/data_set.cc b/paddle/fluid/framework/data_set.cc index a3b7b1e454ecec9da766b9b156c31b1317bb9d35..1b3edeed10352c6abc7cfadadbe16c1aa4e32078 100644 --- a/paddle/fluid/framework/data_set.cc +++ b/paddle/fluid/framework/data_set.cc @@ -141,6 +141,9 @@ template void DatasetImpl::ReleaseMemory() { VLOG(3) << "DatasetImpl::ReleaseMemory() begin"; std::vector().swap(memory_data_); + for (int i = 0; i < readers_.size(); ++i) { + readers_[i]->ReleaseChannelData(); + } VLOG(3) << "DatasetImpl::ReleaseMemory() end"; } @@ -178,8 +181,10 @@ void DatasetImpl::GlobalShuffle() { if (readers_.size() == 0) { CreateReaders(); } - // if it is not InMemory, memory_data_ is empty - std::random_shuffle(memory_data_.begin(), memory_data_.end()); + auto fleet_ptr = FleetWrapper::GetInstance(); + // local shuffle all data before global shuffle + std::shuffle(memory_data_.begin(), memory_data_.end(), + fleet_ptr->LocalRandomEngine()); VLOG(3) << "start global shuffle threads"; std::vector global_shuffle_threads; for (int i = 0; i < thread_num_; ++i) { @@ -260,6 +265,20 @@ void DatasetImpl::DestroyReaders() { } } +template +int64_t DatasetImpl::GetMemoryDataSize() { + return memory_data_.size(); +} + +template +int64_t DatasetImpl::GetShuffleDataSize() { + int64_t sum = 0; + for (int i = 0; i < readers_.size(); ++i) { + sum += readers_[i]->GetChannelDataSize(); + } + return sum; +} + template int DatasetImpl::ReceiveFromClient(int msg_type, int client_id, const std::string& msg) { @@ -267,7 +286,7 @@ int DatasetImpl::ReceiveFromClient(int msg_type, int client_id, VLOG(3) << "ReceiveFromClient msg_type=" << msg_type << ", client_id=" << client_id << ", msg length=" << msg.length(); auto fleet_ptr = FleetWrapper::GetInstance(); - int64_t index = rand_r(&rand_seed) % thread_num_; + int64_t index = fleet_ptr->LocalRandomEngine()() % thread_num_; VLOG(3) << "ramdom index=" << index; readers_[index]->PutInsToChannel(msg); #endif diff --git a/paddle/fluid/framework/data_set.h b/paddle/fluid/framework/data_set.h index bbe0f937abfa635b126062059abfcfb70adb996e..ffbc7bfd95b2bada56b0d6280d05bf678645fb1c 100644 --- a/paddle/fluid/framework/data_set.h +++ b/paddle/fluid/framework/data_set.h @@ -85,6 +85,10 @@ class Dataset { virtual void CreateReaders() = 0; // destroy readers virtual void DestroyReaders() = 0; + // get memory data size + virtual int64_t GetMemoryDataSize() = 0; + // get shuffle data size + virtual int64_t GetShuffleDataSize() = 0; protected: virtual int ReceiveFromClient(int msg_type, int client_id, @@ -127,6 +131,8 @@ class DatasetImpl : public Dataset { virtual void GlobalShuffle(); virtual void CreateReaders(); virtual void DestroyReaders(); + virtual int64_t GetMemoryDataSize(); + virtual int64_t GetShuffleDataSize(); protected: virtual int ReceiveFromClient(int msg_type, int client_id, diff --git a/paddle/fluid/framework/details/CMakeLists.txt b/paddle/fluid/framework/details/CMakeLists.txt index 615cfaa4f31a2411685652c2a7581da6f361eaf3..4eba8177c56b818d5890504c7a5c69e3e317d559 100644 --- a/paddle/fluid/framework/details/CMakeLists.txt +++ b/paddle/fluid/framework/details/CMakeLists.txt @@ -93,6 +93,6 @@ cc_library(build_strategy SRCS build_strategy.cc DEPS fuse_elewise_add_act_pass multi_batch_merge_pass fuse_relu_depthwise_conv_pass memory_optimize_pass lock_free_optimize_pass - alloc_continuous_space_for_grad_pass fuse_all_reduce_op_pass + alloc_continuous_space_for_grad_pass fuse_all_reduce_op_pass backward_optimizer_op_deps_pass fuse_adam_op_pass fuse_sgd_op_pass fuse_momentum_op_pass record_skip_memory_opt_vars_pass) diff --git a/paddle/fluid/framework/details/all_reduce_op_handle.cc b/paddle/fluid/framework/details/all_reduce_op_handle.cc index c9f06c64e447bfbcfeadbb29a5682c8b5b5085a0..2f001e54d4f668537953bbaeb14aa21e6745009f 100644 --- a/paddle/fluid/framework/details/all_reduce_op_handle.cc +++ b/paddle/fluid/framework/details/all_reduce_op_handle.cc @@ -35,16 +35,9 @@ namespace details { AllReduceOpHandle::AllReduceOpHandle(ir::Node *node, const std::vector &local_scopes, const std::vector &places, - const platform::NCCLContextMap *ctxs) - : OpHandleBase(node), - local_scopes_(local_scopes), - places_(places), - nccl_ctxs_(ctxs) { - if (nccl_ctxs_) { - for (auto &p : places_) { - this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p)); - } - } + const platform::NCCLCommunicator *ctxs) + : NCCLOpHandleBase(node, places, ctxs), local_scopes_(local_scopes) { + PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size()); } #else AllReduceOpHandle::AllReduceOpHandle(ir::Node *node, @@ -71,7 +64,9 @@ void AllReduceOpHandle::RunAllReduceFuncs( if (FLAGS_sync_nccl_allreduce) { for (auto &p : places_) { int dev_id = boost::get(p).device; - auto &nccl_ctx = nccl_ctxs_->at(dev_id); + auto *nccl_ctxs = + nccl_ctxs_->GetRunEnvNCCLCtx(run_order_, use_hierarchical_allreduce_); + auto &nccl_ctx = nccl_ctxs->at(dev_id); auto stream = nccl_ctx.stream(); cudaError_t e_sync = cudaStreamSynchronize(stream); if (e_sync != 0) { @@ -134,21 +129,12 @@ void AllReduceOpHandle::RunImpl() { numel = static_cast(lod_tensor.numel()); } - int dev_id = boost::get(p).device; - auto &nccl_ctx = nccl_ctxs_->at(dev_id); - auto stream = nccl_ctx.stream(); - auto comm = nccl_ctx.comm_; - - VLOG(10) << "before all reduce buffer:" << buffer << ", numel:" << numel - << ", dev_id:" << dev_id << ", dtype:" << dtype - << ", place:" << p; - all_reduce_calls.emplace_back([=] { - PADDLE_ENFORCE(platform::dynload::ncclAllReduce( - buffer, buffer, numel, static_cast(dtype), ncclSum, - comm, stream)); + NCCLAllReduce(p, buffer, buffer, numel, + static_cast(dtype), ncclSum); }); } + VLOG(10) << "allreduce size:" << numel * SizeOfType(lod_tensors[0]->type()); RunAllReduceFuncs(all_reduce_calls); #else PADDLE_THROW("Not compiled with CUDA"); diff --git a/paddle/fluid/framework/details/all_reduce_op_handle.h b/paddle/fluid/framework/details/all_reduce_op_handle.h index 3effd0a8517212fdcffc754ba8ab96028f03eaac..f206f5fea5c41536a07143e707c53f135b287035 100644 --- a/paddle/fluid/framework/details/all_reduce_op_handle.h +++ b/paddle/fluid/framework/details/all_reduce_op_handle.h @@ -21,6 +21,7 @@ #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +#include "paddle/fluid/framework/details/nccl_op_handle.h" #include "paddle/fluid/platform/nccl_helper.h" #endif @@ -28,13 +29,15 @@ namespace paddle { namespace framework { namespace details { -class AllReduceOpHandle : public OpHandleBase { - public: #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +class AllReduceOpHandle : public NCCLOpHandleBase { + public: AllReduceOpHandle(ir::Node *node, const std::vector &local_scopes, const std::vector &places, - const platform::NCCLContextMap *ctxs); + const platform::NCCLCommunicator *ctxs); #else +class AllReduceOpHandle : public OpHandleBase { + public: AllReduceOpHandle(ir::Node *node, const std::vector &local_scopes, const std::vector &places); #endif @@ -46,13 +49,17 @@ class AllReduceOpHandle : public OpHandleBase { protected: void RunImpl() override; - std::vector local_scopes_; + +#if !(defined(PADDLE_WITH_CUDA) && !defined(_WIN32)) + // NCCLOpHandleBase already have these attributes. + // Will polish it by class inheritance framework. std::vector places_; +#endif + #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) void RunAllReduceFuncs( const std::vector> &all_reduce_calls); - const platform::NCCLContextMap *nccl_ctxs_; #endif }; diff --git a/paddle/fluid/framework/details/async_ssa_graph_executor.cc b/paddle/fluid/framework/details/async_ssa_graph_executor.cc index 7f63c07b18f7c6147670656dfc567f8f2ae8429a..ce7849cb419950dc2ede4182d108e51bcf6e9945 100644 --- a/paddle/fluid/framework/details/async_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/async_ssa_graph_executor.cc @@ -51,45 +51,39 @@ void ProcessGraph(std::vector graphs, Scope *scope) { VLOG(3) << "ProcessGraph"; RpcCtxMap send_varname_to_ctx; RpcCtxMap recv_varname_to_ctx; - for (auto i = 0; i < graphs.size(); ++i) { - std::vector nodes_to_delete; - for (auto &node : graphs[i]->Nodes()) { - VLOG(3) << "node name " << node->Name(); - if (node && node->IsOp()) { - if (node->Name() == "send") { - auto send_var_name = node->Op()->Input("X")[0]; - auto send_varnames = boost::get>( - node->Op()->GetNullableAttr("send_varnames")); - auto epmap = boost::get>( - node->Op()->GetNullableAttr("epmap")); - auto height_section = boost::get>( - node->Op()->GetNullableAttr("sections")); - auto trainer_id = - boost::get(node->Op()->GetNullableAttr("trainer_id")); - send_varname_to_ctx[send_var_name] = - operators::distributed::RpcContext(send_var_name, send_varnames, - epmap, height_section, - trainer_id); - VLOG(3) << "find and init an send op: " - << send_varname_to_ctx[send_var_name]; - } else if (node->Name() == "recv") { - auto recv_var_name = node->Op()->Output("Out")[0]; - auto recv_varnames = boost::get>( - node->Op()->GetNullableAttr("recv_varnames")); - auto epmap = boost::get>( - node->Op()->GetNullableAttr("epmap")); - auto trainer_id = - boost::get(node->Op()->GetNullableAttr("trainer_id")); - recv_varname_to_ctx[recv_var_name] = - operators::distributed::RpcContext(recv_var_name, recv_varnames, - epmap, {}, trainer_id); - nodes_to_delete.push_back(node); - VLOG(3) << "find and remove an recv op: " - << recv_varname_to_ctx[recv_var_name]; - } + for (auto &node : graphs[0]->Nodes()) { + VLOG(3) << "node name " << node->Name(); + if (node && node->IsOp()) { + if (node->Name() == "send") { + auto send_var_name = node->Op()->Input("X")[0]; + auto send_varnames = boost::get>( + node->Op()->GetNullableAttr("send_varnames")); + auto epmap = boost::get>( + node->Op()->GetNullableAttr("epmap")); + auto height_section = boost::get>( + node->Op()->GetNullableAttr("sections")); + auto trainer_id = + boost::get(node->Op()->GetNullableAttr("trainer_id")); + send_varname_to_ctx[send_var_name] = operators::distributed::RpcContext( + send_var_name, send_varnames, epmap, height_section, trainer_id); + VLOG(3) << "find and init an send op: " + << send_varname_to_ctx[send_var_name]; + } else if (node->Name() == "recv") { + auto recv_var_name = node->Op()->Output("Out")[0]; + auto recv_varnames = boost::get>( + node->Op()->GetNullableAttr("recv_varnames")); + auto epmap = boost::get>( + node->Op()->GetNullableAttr("epmap")); + auto trainer_id = + boost::get(node->Op()->GetNullableAttr("trainer_id")); + recv_varname_to_ctx[recv_var_name] = operators::distributed::RpcContext( + recv_var_name, recv_varnames, epmap, {}, trainer_id); + VLOG(3) << "find and remove an recv op: " + << recv_varname_to_ctx[recv_var_name]; } } } + // init communicator here if (send_varname_to_ctx.size() > 0) { VLOG(3) << "this is distribute mode, will use communicator"; diff --git a/paddle/fluid/framework/details/build_strategy.cc b/paddle/fluid/framework/details/build_strategy.cc index 845fdf511e455509ff3e871084c17163c90c674a..3b57a099c8afeeca05f9fa45eda78e20197dc798 100644 --- a/paddle/fluid/framework/details/build_strategy.cc +++ b/paddle/fluid/framework/details/build_strategy.cc @@ -16,6 +16,7 @@ limitations under the License. */ #include #include +#include #include #include "paddle/fluid/framework/details/reduce_op_handle.h" #include "paddle/fluid/framework/ir/graph.h" @@ -26,6 +27,8 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h" #include "paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_print_pass.h" +DECLARE_bool(use_mkldnn); + namespace paddle { namespace framework { namespace details { @@ -46,6 +49,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder { : ir::PassBuilder(), strategy_(strategy) { // Add a graph viz pass to record a graph. if (!strategy_.debug_graphviz_path_.empty()) { + VLOG(1) << "Add graph_viz_pass"; auto viz_pass = AppendPass("graph_viz_pass"); const std::string graph_path = string::Sprintf( "%s%s", strategy_.debug_graphviz_path_.c_str(), "_original_graph"); @@ -53,10 +57,27 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder { } // Note(zcd): record_skip_memory_opt_vars_pass should be the first pass. + VLOG(1) << "Add record_skip_memory_opt_vars_pass"; AppendPass("record_skip_memory_opt_vars_pass"); +#ifdef PADDLE_WITH_MKLDNN + if (FLAGS_use_mkldnn) { + VLOG(1) << "Add mkldnn_placement_pass"; + AppendPass("mkldnn_placement_pass"); + } else if (!strategy_.mkldnn_enabled_op_types_.empty()) { + LOG(WARNING) + << "mkldnn_enabled_op_types specify the operator type list to " + "use MKLDNN acceleration. It is null in default, means " + "that all the operators supported by MKLDNN will be " + "accelerated. And it should not be set when " + "FLAGS_use_mkldnn=false."; + } +#else + PADDLE_ENFORCE(!FLAGS_use_mkldnn, + "Please compile with MKLDNN first to use MKLDNN"); +#endif if (strategy_.enable_sequential_execution_) { - VLOG(5) << "Add sequential_execution_pass"; + VLOG(1) << "Add sequential_execution_pass"; AppendPass("sequential_execution_pass"); } @@ -67,7 +88,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder { // Add op fusion. if (strategy.fuse_relu_depthwise_conv_) { - VLOG(5) << "Add fuse_relu_depthwise_conv_pass"; + VLOG(1) << "Add fuse_relu_depthwise_conv_pass"; AppendPass("fuse_relu_depthwise_conv_pass"); } @@ -79,19 +100,19 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder { // Add automatically inplace. if (strategy_.enable_inplace_) { - VLOG(5) << "Add inplace_pass"; + VLOG(1) << "Add inplace_pass"; AppendPass("inplace_pass"); } if (strategy_.fuse_elewise_add_act_ops_) { - VLOG(5) << "Add fuse_elewise_add_act_pass"; + VLOG(1) << "Add fuse_elewise_add_act_pass"; AppendPass("fuse_elewise_add_act_pass"); } // for single card training, fuse_all_reduce_ops is unnecessary. // alloc_continuous_space_for_grad_pass should be before of MultiDevPass. if (strategy_.fuse_all_reduce_ops_) { - VLOG(5) << "Add alloc_continuous_space_for_grad_pass"; + VLOG(1) << "Add alloc_continuous_space_for_grad_pass"; AppendPass("alloc_continuous_space_for_grad_pass"); } @@ -106,11 +127,11 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder { // NOTE: fuse_all_xx_ops will count the number of xx operator first, // if the number is zero, fuse_all_reduce_ops will do nothing. // Currently, only one type of optimization algorithm can be fused. - VLOG(5) << "Add fuse_adam_op_pass"; + VLOG(1) << "Add fuse_adam_op_pass"; AppendPass("fuse_adam_op_pass"); - VLOG(5) << "Add fuse_sgd_op_pass"; + VLOG(1) << "Add fuse_sgd_op_pass"; AppendPass("fuse_sgd_op_pass"); - VLOG(5) << "Add fuse_momentum_op_pass"; + VLOG(1) << "Add fuse_momentum_op_pass"; AppendPass("fuse_momentum_op_pass"); } } @@ -140,7 +161,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder { // A side-effect of that, memory optimize cannot forsee the fetched vars // , so fetchlist should be set persistable before call the Run interface. if (strategy_.memory_optimize_) { - VLOG(5) << "Add memory_optimize_pass"; + VLOG(1) << "Add memory_optimize_pass"; AppendPass("memory_optimize_pass"); } @@ -148,26 +169,22 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder { // all original and fused operators. But no operators can be enabled this // attr if putting it after MultiDevPass. if (strategy_.cache_runtime_context_) { - VLOG(5) << "Add runtime_context_cache_pass"; + VLOG(1) << "Add runtime_context_cache_pass"; AppendPass("runtime_context_cache_pass"); } - if (strategy_.cache_expected_kernel_) { - VLOG(10) << "Add expected_kernel_cache_pass"; - AppendPass("expected_kernel_cache_pass"); - } - AppendMultiDevPass(strategy_); if (strategy_.fuse_all_reduce_ops_) { // NOTE: fuse_all_reduce_ops will count the number of all_reduce operator // first, if the number is zero, fuse_all_reduce_ops will do nothing. - VLOG(5) << "Add fuse_all_reduce_op_pass"; + VLOG(1) << "Add fuse_all_reduce_op_pass"; AppendPass("fuse_all_reduce_op_pass"); } // Add a graph print pass to record a graph with device info. if (!strategy_.debug_graphviz_path_.empty()) { + VLOG(1) << "Add multi_devices_print_pass"; auto multi_devices_print_pass = AppendPass("multi_devices_print_pass"); const std::string graph_path = string::Sprintf("%s%s", strategy_.debug_graphviz_path_.c_str(), @@ -183,16 +200,22 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder { if (!strategy_.enable_parallel_graph_ && (SeqOnlyAllReduceOps(strategy_) || strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce)) { - VLOG(5) << "Add all_reduce_deps_pass"; + VLOG(1) << "Add all_reduce_deps_pass"; AppendPass("all_reduce_deps_pass"); } + if (strategy_.enable_backward_optimizer_op_deps_) { + VLOG(1) << "Add backward_op_deps_pass"; + AppendPass("backward_optimizer_op_deps_pass"); + } + if (strategy_.remove_unnecessary_lock_) { - VLOG(5) << "Add modify_op_lock_and_record_event_pass"; + VLOG(1) << "Add modify_op_lock_and_record_event_pass"; AppendPass("modify_op_lock_and_record_event_pass"); } // Verify that the graph is correct for multi-device executor. + VLOG(1) << "Add multi_devices_check_pass"; AppendPass("multi_devices_check_pass"); } @@ -201,18 +224,19 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder { ir::Pass *multi_devices_pass = nullptr; if (strategy_.async_mode_) { + VLOG(1) << "Add async_multi_devices_pass"; multi_devices_pass = AppendPass("async_multi_devices_pass").get(); } else if (strategy_.is_distribution_) { - VLOG(5) + VLOG(1) << "Add dist_multi_devices_pass, multi device parameter server mode"; multi_devices_pass = AppendPass("dist_multi_devices_pass").get(); } else { if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) { - VLOG(5) << "Add all_reduce_mode_multi_devices_pass"; + VLOG(1) << "Add all_reduce_mode_multi_devices_pass"; multi_devices_pass = AppendPass("all_reduce_mode_multi_devices_pass").get(); } else if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kReduce) { - VLOG(5) << "Add reduce_mode_multi_devices_pass"; + VLOG(1) << "Add reduce_mode_multi_devices_pass"; multi_devices_pass = AppendPass("reduce_mode_multi_devices_pass").get(); } else { PADDLE_THROW("Unknown reduce strategy."); @@ -249,7 +273,7 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph, const size_t &nranks, #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) const bool use_cuda, - platform::NCCLContextMap *nccl_ctxs) const { + platform::NCCLCommunicator *nccl_ctxs) const { #else const bool use_cuda) const { #endif @@ -271,9 +295,9 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph, pass->Set(ir::kNRanks, new size_t(nranks)); #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr; + platform::NCCLCommunicator *nctx = use_cuda ? nccl_ctxs : nullptr; pass->Erase(kNCCLCtxs); - pass->SetNotOwned(kNCCLCtxs, nctx); + pass->SetNotOwned(kNCCLCtxs, nctx); #endif } else if (pass->Type() == "alloc_continuous_space_for_grad_pass" || pass->Type() == "fuse_adam_op_pass" || @@ -287,9 +311,12 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph, &local_scopes); if (pass->Type() == "fuse_all_reduce_op_pass") { #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr; + platform::NCCLCommunicator *nctx = use_cuda ? nccl_ctxs : nullptr; pass->Erase(kNCCLCtxs); - pass->SetNotOwned(kNCCLCtxs, nctx); + pass->SetNotOwned(kNCCLCtxs, nctx); + pass->Erase(kUseHierarchicalAllReduce); + pass->Set(kUseHierarchicalAllReduce, + new bool(use_hierarchical_allreduce_)); #endif } } else if (pass->Type() == "alloc_continuous_space_for_grad_pass") { @@ -302,6 +329,14 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph, LOG(INFO) << "set enable_sequential_execution:" << enable_sequential_execution_; } else if (pass->Type() == "all_reduce_deps_pass") { +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) + platform::NCCLCommunicator *nctx = use_cuda ? nccl_ctxs : nullptr; + pass->Erase(kNCCLCtxs); + pass->SetNotOwned(kNCCLCtxs, nctx); + pass->Erase(kUseHierarchicalAllReduce); + pass->Set(kUseHierarchicalAllReduce, + new bool(use_hierarchical_allreduce_)); +#endif LOG(INFO) << "SeqOnlyAllReduceOps:" << SeqOnlyAllReduceOps(*this) << ", num_trainers:" << num_trainers_; } else if (pass->Type() == "fuse_relu_depthwise_conv_pass") { @@ -313,6 +348,9 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph, } else if (pass->Type() == "inplace_pass") { pass->Erase(ir::kUseCuda); pass->Set(ir::kUseCuda, new bool(use_cuda)); + } else if (pass->Type() == "mkldnn_placement_pass") { + pass->Set("mkldnn_enabled_op_types", + new std::unordered_set(mkldnn_enabled_op_types_)); } VLOG(3) << "Start Apply Pass " << pass->Type(); graph = pass->Apply(graph); @@ -339,6 +377,7 @@ USE_PASS(multi_devices_print_pass); USE_PASS(memory_optimize_pass); USE_PASS(sequential_execution_pass); USE_PASS(all_reduce_deps_pass); +USE_PASS(backward_optimizer_op_deps_pass); USE_PASS(modify_op_lock_and_record_event_pass); USE_PASS(inplace_pass); USE_PASS(lock_free_optimize_pass); @@ -349,5 +388,7 @@ USE_PASS(fuse_sgd_op_pass); USE_PASS(fuse_momentum_op_pass); USE_PASS(fuse_all_reduce_op_pass); USE_PASS(runtime_context_cache_pass); -USE_PASS(expected_kernel_cache_pass); USE_PASS(record_skip_memory_opt_vars_pass); +#ifdef PADDLE_WITH_MKLDNN +USE_PASS(mkldnn_placement_pass); +#endif diff --git a/paddle/fluid/framework/details/build_strategy.h b/paddle/fluid/framework/details/build_strategy.h index b1601cfbcd5e9c66f1bbecd1f6fe10bc279cea26..8eaace17bb1a59bc5033e632511886c7630d0cd2 100644 --- a/paddle/fluid/framework/details/build_strategy.h +++ b/paddle/fluid/framework/details/build_strategy.h @@ -16,6 +16,7 @@ #include #include +#include #include #include #include "paddle/fluid/framework/ir/pass_builder.h" @@ -79,6 +80,8 @@ struct BuildStrategy { bool fuse_all_reduce_ops_{false}; + bool enable_backward_optimizer_op_deps_{false}; + bool fuse_relu_depthwise_conv_{false}; bool sync_batch_norm_{false}; @@ -108,7 +111,18 @@ struct BuildStrategy { bool remove_unnecessary_lock_{true}; bool cache_runtime_context_{false}; - bool cache_expected_kernel_{true}; + std::unordered_set mkldnn_enabled_op_types_; + + size_t nccl_comm_num_{1}; + // The picture is here: + // https://github.com/PaddlePaddle/Paddle/pull/17263#discussion_r285411396 + bool use_hierarchical_allreduce_{false}; + // Nccl ranks in a node when use hierarchical allreduce, it's setted to gpu + // cards' number in most cases. + size_t hierarchical_allreduce_inter_nranks_{0}; + // Nccl ranks bewteen nodes when use hierarchical allreduce, it's setted to + // nodes number. + size_t hierarchical_allreduce_exter_nranks_{0}; // NOTE: // Before you add new options, think if it's a general strategy that works @@ -135,7 +149,7 @@ struct BuildStrategy { const size_t &nranks, #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) const bool use_cuda, - platform::NCCLContextMap *nccl_ctxs) const; + platform::NCCLCommunicator *nccl_ctxs) const; #else const bool use_cuda) const; #endif diff --git a/paddle/fluid/framework/details/eager_deletion_op_handle.cc b/paddle/fluid/framework/details/eager_deletion_op_handle.cc index c8e27c7275fe70598e41cbb2cc8482d610c2e113..f8723fe75f8f0304e149ab2195f29bc4c7223bc4 100644 --- a/paddle/fluid/framework/details/eager_deletion_op_handle.cc +++ b/paddle/fluid/framework/details/eager_deletion_op_handle.cc @@ -20,6 +20,7 @@ #include "paddle/fluid/framework/lod_tensor_array.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/selected_rows.h" +#include "paddle/fluid/platform/profiler.h" #ifdef PADDLE_WITH_CUDA #include "paddle/fluid/platform/cuda_device_guard.h" #endif @@ -65,6 +66,7 @@ EagerDeletionOpHandle::~EagerDeletionOpHandle() { std::string EagerDeletionOpHandle::Name() const { return "eager_deletion"; } void EagerDeletionOpHandle::RunImpl() { + platform::RecordEvent record_event(Name()); Scope *exec_scope = nullptr; std::deque> garbages; for (auto &name : var_names_) { diff --git a/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc b/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc index c69f148297aa01c4741afa3d50f11f9fb02b3b6f..b33162edd2b69ca0703f27041e71fe72da9779e3 100644 --- a/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc @@ -20,6 +20,7 @@ #include "paddle/fluid/framework/details/fetch_op_handle.h" #include "paddle/fluid/framework/details/multi_devices_helper.h" #include "paddle/fluid/framework/ir/graph_helper.h" +#include "paddle/fluid/platform/profiler.h" namespace paddle { namespace framework { @@ -43,35 +44,97 @@ FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor( bootstrap_ops_.emplace_back(op); } } - + PADDLE_ENFORCE_GT(op_deps_.size(), 0, "The graph doesn't have operators."); PrepareAtomicOpDeps(); } FeedFetchList FastThreadedSSAGraphExecutor::Run( const std::vector &fetch_tensors) { + VLOG(3) << "enter FastThreadedSSAGraphExecutor Run"; + std::unique_ptr event( + new platform::RecordEvent("FastThreadedSSAGraphExecutorPrepare")); std::unique_ptr>> op_deps = atomic_op_deps_.get(); PrepareAtomicOpDeps(); + size_t num_ops = op_deps->size(); paddle::framework::FeedFetchList fetches; fetches.resize(fetch_tensors.size()); std::unordered_map> fetched_vars; - std::vector fetch_ops; + std::vector fetch_ops; std::vector ready_fetch_ops; + exception_.Clear(); + + InsertFetchOps(fetch_tensors, &fetches, &fetched_vars, op_deps.get(), + &fetch_ops, &ready_fetch_ops); + event.reset(nullptr); + if (strategy_.num_threads_ == 1 && traced_ops_.size() == num_ops) { + // If the num_threads is 1, we can record the order of operator's + // execution in the first iteration, and in subsequent iterations, + // run the recorded operators directly. This strategy could make the + // execution faster. + VLOG(3) << "Run the traced ops."; + RunTracedOps(traced_ops_); + RunTracedOps(fetch_ops); + if (exception_.IsCaught()) { + ExecutionFinal(&fetch_ops); + } + } else { + traced_ops_.clear(); + remaining_ = 0; + auto complete_q = std::make_shared>(); + for (auto op : bootstrap_ops_) { + RunOpAsync(op_deps.get(), op, complete_q); + } + for (auto op : ready_fetch_ops) { + RunOpAsync(op_deps.get(), op, complete_q); + } + + size_t num_complete = 0; + while (num_complete != op_deps->size()) { + size_t num_comp = complete_q->Pop(); + if (num_comp == -1UL) { + int remaining = 0; + while (true) { + remaining = remaining_; + if (remaining == 0) { + break; + } + for (int i = 0; i < remaining; ++i) { + complete_q->Pop(); + } + } + if (exception_.IsCaught()) { + ExecutionFinal(&fetch_ops); + } + } + num_complete += num_comp; + } + } + // Wait FetchOps. + ClearFetchOp(graph_, &fetch_ops); + return fetches; +} +void FastThreadedSSAGraphExecutor::InsertFetchOps( + const std::vector &fetch_tensors, FeedFetchList *fetches, + std::unordered_map> *fetched_vars, + std::unordered_map> *op_deps, + std::vector *fetch_ops, + std::vector *ready_fetch_ops) { for (auto &fetch_var_name : fetch_tensors) { - for (auto &var_map : graph_->Get(details::kGraphVars)) { + for (auto &var_map : graph_->Get(kGraphVars)) { auto it = var_map.find(fetch_var_name); if (it != var_map.end()) { - fetched_vars[fetch_var_name].push_back(*it->second.rbegin()); + (*fetched_vars)[fetch_var_name].push_back(*it->second.rbegin()); } } } for (size_t i = 0; i < fetch_tensors.size(); ++i) { - auto &var_name = fetch_tensors[i]; - auto fetched_var_it = fetched_vars.find(var_name); - PADDLE_ENFORCE(fetched_var_it != fetched_vars.end(), + auto &var_name = fetch_tensors.at(i); + auto fetched_var_it = fetched_vars->find(var_name); + PADDLE_ENFORCE(fetched_var_it != fetched_vars->end(), "Cannot find fetched variable(%s).(Perhaps the main_program " "is not set to ParallelExecutor)", var_name); @@ -80,8 +143,8 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run( ir::Node *fetch_node = graph_->CreateEmptyNode("fetch", ir::Node::Type::kOperation); - auto *op = new FetchOpHandle(fetch_node, &fetches, i, &local_scopes_); - fetch_ops.emplace_back(op); + auto *op = new FetchOpHandle(fetch_node, fetches, i, &local_scopes_); + fetch_ops->emplace_back(op); for (auto &p : places_) { op->SetDeviceContext(p, fetch_ctxs_.Get(p)); @@ -94,55 +157,22 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run( int dep = static_cast(op->NotReadyInputSize()); (*op_deps)[op] = dep; if (dep == 0) { - ready_fetch_ops.emplace_back(op); - } - } - - size_t num_complete = 0; - remaining_ = 0; - auto complete_q = std::make_shared>(); - for (auto op : bootstrap_ops_) { - RunOpAsync(op_deps.get(), op, complete_q); - } - for (auto op : ready_fetch_ops) { - RunOpAsync(op_deps.get(), op, complete_q); - } - while (num_complete != op_deps->size()) { - size_t num_comp = complete_q->Pop(); - if (num_comp == -1UL) { - int remaining = 0; - while (true) { - remaining = remaining_; - if (remaining == 0) { - break; - } - for (int i = 0; i < remaining; ++i) { - complete_q->Pop(); - } - } - if (exception_.IsCaught()) { - ClearFetchOp(graph_, &fetch_ops); - exception_.ReThrow(); - } + ready_fetch_ops->emplace_back(op); } - num_complete += num_comp; } - // Wait FetchOps. - ClearFetchOp(graph_, &fetch_ops); - return fetches; } bool FastThreadedSSAGraphExecutor::RunOp( OpHandleBase *op, const std::shared_ptr> &complete_q, size_t *complete) { - try { + RunOpSync(op); + if (LIKELY(!exception_.IsCaught())) { if (LIKELY(!strategy_.dry_run_)) { - op->Run(strategy_.use_cuda_); + RecordOps(op); } ++(*complete); return true; - } catch (...) { - exception_.Catch(std::current_exception()); + } else { --remaining_; complete_q->Push(-1UL); return false; @@ -194,6 +224,7 @@ void FastThreadedSSAGraphExecutor::RunOpAsync( complete_q->Push(complete); }); } + void FastThreadedSSAGraphExecutor::PrepareAtomicOpDeps() { atomic_op_deps_ = prepare_pool_.enqueue([&] { auto *op_deps = new std::unordered_map>; @@ -206,6 +237,44 @@ void FastThreadedSSAGraphExecutor::PrepareAtomicOpDeps() { } const ir::Graph &FastThreadedSSAGraphExecutor::Graph() const { return *graph_; } + +void FastThreadedSSAGraphExecutor::RecordOps(OpHandleBase *op) { + if (strategy_.num_threads_ == 1 && !dynamic_cast(op)) { + traced_ops_.emplace_back(op); + } +} + +void FastThreadedSSAGraphExecutor::ExecutionFinal( + std::vector *fetch_ops) { + VLOG(3) << "caught exception " << exception_.Type() << ", rethrow it"; + ClearFetchOp(graph_, fetch_ops); + exception_.ReThrow(); +} + +void FastThreadedSSAGraphExecutor::RunTracedOps( + const std::vector &traced_ops) { + for (auto &op : traced_ops) { + if (exception_.IsCaught()) { + return; + } + RunOpSync(op); + } +} + +void FastThreadedSSAGraphExecutor::RunOpSync(OpHandleBase *op) { + try { + if (VLOG_IS_ON(10)) { + VLOG(10) << op << " " << op->Name() << " : " << op->DebugString(); + } + if (LIKELY(!strategy_.dry_run_)) { + op->Run(strategy_.use_cuda_); + } + VLOG(10) << op << " " << op->Name() << " Done "; + } catch (...) { + exception_.Catch(std::current_exception()); + } +} + } // namespace details } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h b/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h index 234da5b9254bcdfb4682301c679be67f99cda280..d88e5bbaa97419c6e5229deaa16fbcfa922432d0 100644 --- a/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h +++ b/paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h @@ -60,6 +60,8 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor { ::ThreadPool pool_; ::ThreadPool prepare_pool_; + std::vector traced_ops_; + bool RunOp(OpHandleBase *op, const std::shared_ptr> &complete_q, size_t *complete); @@ -69,6 +71,22 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor { const std::shared_ptr> &complete_q); void PrepareAtomicOpDeps(); + + inline void RecordOps(OpHandleBase *op); + + inline void ExecutionFinal(std::vector *fetch_ops); + + inline void RunOpSync(OpHandleBase *op); + + void RunTracedOps(const std::vector &traced_ops); + + void InsertFetchOps( + const std::vector &fetch_tensors, FeedFetchList *fetches, + std::unordered_map> + *fetched_vars, + std::unordered_map> *op_deps, + std::vector *fetch_ops, + std::vector *ready_fetch_ops); }; } // namespace details } // namespace framework diff --git a/paddle/fluid/framework/details/fused_all_reduce_op_handle.cc b/paddle/fluid/framework/details/fused_all_reduce_op_handle.cc index a57d670f118f2eb0bdcbeb7ed080729e4f9e4f2b..4d96d820a1d161e76945a1c87e1832d95a8a802e 100644 --- a/paddle/fluid/framework/details/fused_all_reduce_op_handle.cc +++ b/paddle/fluid/framework/details/fused_all_reduce_op_handle.cc @@ -44,17 +44,10 @@ typedef std::vector>> FusedAllReduceOpHandle::FusedAllReduceOpHandle( ir::Node *node, const std::vector &local_scopes, const std::vector &places, const size_t num_of_all_reduce, - const platform::NCCLContextMap *ctxs) - : OpHandleBase(node), + const platform::NCCLCommunicator *ctxs) + : NCCLOpHandleBase(node, places, ctxs), local_scopes_(local_scopes), - places_(places), - num_of_all_reduce_(num_of_all_reduce), - nccl_ctxs_(ctxs) { - if (nccl_ctxs_) { - for (auto &p : places_) { - this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p)); - } - } + num_of_all_reduce_(num_of_all_reduce) { PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size()); } #else @@ -167,17 +160,14 @@ void FusedAllReduceOpHandle::RunImpl() { auto &p = places_[i]; void *buffer = const_cast(lod_tensor_data.at(i)); - int dev_id = boost::get(p).device; - auto &nccl_ctx = nccl_ctxs_->at(dev_id); - auto stream = nccl_ctx.stream(); - auto comm = nccl_ctx.comm_; all_reduce_calls.emplace_back([=] { - PADDLE_ENFORCE(platform::dynload::ncclAllReduce( - buffer, buffer, numel, static_cast(nccl_dtype), - ncclSum, comm, stream)); + NCCLAllReduce(p, buffer, buffer, numel, + static_cast(nccl_dtype), ncclSum); }); } + VLOG(10) << "fusedallreduce size:" << numel * SizeOfType(dtype); + this->RunAndRecordEvent([&] { if (all_reduce_calls.size() == 1UL) { // Do not use NCCLGroup when manage NCCL by per thread per device diff --git a/paddle/fluid/framework/details/fused_all_reduce_op_handle.h b/paddle/fluid/framework/details/fused_all_reduce_op_handle.h index 79772c61f8c8b7abe3cf26dd8a94c2acdc0872a0..e0b9123c5b7e40f7d96ef3ea4061c2822aca7eef 100644 --- a/paddle/fluid/framework/details/fused_all_reduce_op_handle.h +++ b/paddle/fluid/framework/details/fused_all_reduce_op_handle.h @@ -21,6 +21,7 @@ #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +#include "paddle/fluid/framework/details/nccl_op_handle.h" #include "paddle/fluid/platform/nccl_helper.h" #endif @@ -28,14 +29,15 @@ namespace paddle { namespace framework { namespace details { -struct FusedAllReduceOpHandle : public OpHandleBase { #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +struct FusedAllReduceOpHandle : public NCCLOpHandleBase { FusedAllReduceOpHandle(ir::Node *node, const std::vector &local_scopes, const std::vector &places, const size_t num_of_all_reduce, - const platform::NCCLContextMap *ctxs); + const platform::NCCLCommunicator *ctxs); #else +struct FusedAllReduceOpHandle : public OpHandleBase { FusedAllReduceOpHandle(ir::Node *node, const std::vector &local_scopes, const std::vector &places, @@ -52,11 +54,12 @@ struct FusedAllReduceOpHandle : public OpHandleBase { private: std::vector local_scopes_; +#if !(defined(PADDLE_WITH_CUDA) && !defined(_WIN32)) + // NCCLOpHandleBase already have these attributes. + // Will polish it by class inheritance framework. std::vector places_; - size_t num_of_all_reduce_; -#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - const platform::NCCLContextMap *nccl_ctxs_; #endif + size_t num_of_all_reduce_; // Check the dtype of the input void GetDTypeAndNumel( diff --git a/paddle/fluid/framework/details/multi_devices_helper.h b/paddle/fluid/framework/details/multi_devices_helper.h index 6e6ef074db3450ebbb5567743b908e0aee382c27..e97e5f439d15e2502843389dfaf95772ff5c7971 100644 --- a/paddle/fluid/framework/details/multi_devices_helper.h +++ b/paddle/fluid/framework/details/multi_devices_helper.h @@ -45,6 +45,7 @@ constexpr char kGraphVars[] = "vars"; constexpr char kPlaces[] = "places"; constexpr char kLocalScopes[] = "local_scopes"; constexpr char kNCCLCtxs[] = "nccl_ctxs"; +constexpr char kUseHierarchicalAllReduce[] = "use_hierarchical_allreduce"; // aux variables to represent dependency. Useful to resolve data hazard. typedef std::unordered_set GraphDepVars; diff --git a/paddle/fluid/framework/details/op_handle_base.cc b/paddle/fluid/framework/details/op_handle_base.cc index 69cd84ebf2d678c089141f09a92c46e3a03fe4d9..b0e6a87bddeecda4f13e1081efeabb1c70be76cf 100644 --- a/paddle/fluid/framework/details/op_handle_base.cc +++ b/paddle/fluid/framework/details/op_handle_base.cc @@ -20,7 +20,7 @@ namespace framework { namespace details { std::string OpHandleBase::DebugString() const { std::stringstream ss; - ss << "("; + ss << Name() << "("; for (auto *var : inputs_) { ss << var->DebugString() << ", "; } @@ -187,6 +187,11 @@ void OpHandleBase::RunAndRecordEvent(const std::function &callback) { std::function method = callback; for (auto &p : dev_ctxes_) { method = [method, p, this]() { + VLOG(10) << "cudadevicecontext:" + << static_cast(p.second) + << ", dev_id:" + << boost::get(p.first).device; + static_cast(p.second)->RecordEvent( events_.at(boost::get(p.first).device), method); diff --git a/paddle/fluid/framework/details/parallel_ssa_graph_executor.cc b/paddle/fluid/framework/details/parallel_ssa_graph_executor.cc index 1bd27263f7dad5f733c553c202444ba7cacd2510..68be353e3464c94e5eb991acc4c3dd6e3de5267a 100644 --- a/paddle/fluid/framework/details/parallel_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/parallel_ssa_graph_executor.cc @@ -95,6 +95,7 @@ ParallelSSAGraphExecutor::ParallelSSAGraphExecutor( auto seq_allreduce_pass = ir::PassRegistry::Instance().Get("all_reduce_deps_pass"); + seq_allreduce_pass->Set(kUseHierarchicalAllReduce, new bool(false)); for (size_t i = 0; i < graphs_.size(); ++i) { graphs_[i].reset(seq_allreduce_pass->Apply(graphs_[i].release())); } diff --git a/paddle/fluid/framework/details/rpc_op_handle.cc b/paddle/fluid/framework/details/rpc_op_handle.cc index 3e082f247adf7fe22db2b62802f0a87c9c93447a..a87b03451bb00643ecb9d9e2339141fe7f25d2e3 100644 --- a/paddle/fluid/framework/details/rpc_op_handle.cc +++ b/paddle/fluid/framework/details/rpc_op_handle.cc @@ -14,6 +14,7 @@ #include "paddle/fluid/framework/details/rpc_op_handle.h" #include "paddle/fluid/framework/ir/graph.h" +#include "paddle/fluid/platform/profiler.h" namespace paddle { namespace framework { @@ -29,6 +30,8 @@ RPCOpHandle::RPCOpHandle(ir::Node *node, const framework::OpDesc &op_desc, place_(place) {} void RPCOpHandle::RunImpl() { + platform::RecordEvent record_event(Name()); + for (auto *in : inputs_) { auto &p = static_cast(in)->place(); if (ir::IsControlDepVar(*in->Node())) { diff --git a/paddle/fluid/framework/details/scale_loss_grad_op_handle.cc b/paddle/fluid/framework/details/scale_loss_grad_op_handle.cc index 6924549f36d6365534ab288257899a78107675cc..67b4fed0d3083b105eae4838cf264bba7f7a44c3 100644 --- a/paddle/fluid/framework/details/scale_loss_grad_op_handle.cc +++ b/paddle/fluid/framework/details/scale_loss_grad_op_handle.cc @@ -13,8 +13,8 @@ // limitations under the License. #include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h" - #include +#include "paddle/fluid/platform/profiler.h" namespace paddle { namespace framework { @@ -67,6 +67,7 @@ struct ScaleLossGradFunctor { }; void ScaleLossGradOpHandle::RunImpl() { + platform::RecordEvent record_event(Name()); // Doesn't wait any event std::string var_name = static_cast(this->outputs_[0])->name(); auto &local_scope = *scope_->FindVar(kLocalExecScopeName)->Get(); diff --git a/paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc b/paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc index 247d78479348da998a46d7838b89c481c9e299e5..5bbbf07e6d9fb8845d3f93d1d8124d3f557dba3c 100644 --- a/paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc @@ -36,26 +36,10 @@ ScopeBufferedSSAGraphExecutor::ScopeBufferedSSAGraphExecutor( FeedFetchList ScopeBufferedSSAGraphExecutor::Run( const std::vector &fetch_tensors) { if (drop_scope_counter_ == 0) { - // Create local scopes. - for (auto it = local_scopes_.rbegin(); it != local_scopes_.rend(); ++it) { - auto &scope = *it; - Scope &local_scope = scope->NewScope(); - *scope->Var(details::kLocalExecScopeName)->GetMutable() = - &local_scope; - - for (auto &info : var_infos_) { - if (scope->FindVar(info.name_) != nullptr) { - continue; - } - - if (info.persistable_) { // Persistable - InitializeVariable(scope->Var(info.name_), info.type_); - } else { - InitializeVariable(local_scope.Var(info.name_), info.type_); - } - } - } + platform::RecordEvent e("InitLocalExeScopes"); + PrepareLocalExeScopes(); } + std::vector fetch_data; std::exception_ptr eptr = nullptr; try { @@ -64,9 +48,7 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run( eptr = std::current_exception(); } - platform::RecordEvent e("ScopeBufferedSSAGraphExecutorAfterRun"); ++drop_scope_counter_; - if (drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) { DropLocalExeScopes(); } @@ -78,16 +60,40 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run( } void ScopeBufferedSSAGraphExecutor::DropLocalExeScopes() { + platform::RecordEvent drop_scope_event("DropLocalExeScopes"); drop_scope_counter_ = 0; for (auto p : places_) { platform::DeviceContextPool::Instance().Get(p)->Wait(); } for (auto &scope : local_scopes_) { - auto &local_scope = - *scope->Var(details::kLocalExecScopeName)->GetMutable(); - scope->DeleteScope(local_scope); - VLOG(3) << "Drop local execution scope: " << local_scope; + auto *local_scope_var = scope->FindLocalVar(details::kLocalExecScopeName); + if (local_scope_var != nullptr) { + auto &local_scope = *local_scope_var->GetMutable(); + scope->DeleteScope(local_scope); + scope->EraseVars({std::string(details::kLocalExecScopeName)}); + VLOG(3) << "Drop local execution scope: " << local_scope; + } + } +} + +void ScopeBufferedSSAGraphExecutor::PrepareLocalExeScopes() { + // Create local scopes. + for (auto it = local_scopes_.rbegin(); it != local_scopes_.rend(); ++it) { + auto &scope = *it; + Scope &local_scope = scope->NewScope(); + *scope->Var(kLocalExecScopeName)->GetMutable() = &local_scope; + + for (auto &info : var_infos_) { + if (scope->FindVar(info.name_) != nullptr) { + continue; + } + if (info.persistable_) { // Persistable + InitializeVariable(scope->Var(info.name_), info.type_); + } else { + InitializeVariable(local_scope.Var(info.name_), info.type_); + } + } } } diff --git a/paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h b/paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h index 030777cad894fa24ccdc0afa1aae8e7e4caa90ee..e0388be305f2285b941bc7193a8d97e52ce765c9 100644 --- a/paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h +++ b/paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h @@ -13,7 +13,8 @@ // limitations under the License. #pragma once - +#include +#include #include #include #include @@ -51,6 +52,8 @@ class ScopeBufferedSSAGraphExecutor : public SSAGraphExecutor { bool NeedCreateLocalExeScope(); + void PrepareLocalExeScopes(); + private: size_t drop_scope_counter_{0}; ExecutionStrategy strategy_; diff --git a/paddle/fluid/framework/details/sparse_all_reduce_op_handle.cc b/paddle/fluid/framework/details/sparse_all_reduce_op_handle.cc index 1bdd33fd5357c839aed008d03a9a99848c66101b..cc3493d849eccbecf3d039dc7b2fc18575fcf9d0 100644 --- a/paddle/fluid/framework/details/sparse_all_reduce_op_handle.cc +++ b/paddle/fluid/framework/details/sparse_all_reduce_op_handle.cc @@ -30,7 +30,7 @@ namespace details { SparseAllReduceOpHandle::SparseAllReduceOpHandle( ir::Node *node, const std::vector &local_scopes, const std::vector &places, - const platform::NCCLContextMap *ctxs, bool is_encoded, int nranks) + const platform::NCCLCommunicator *ctxs, bool is_encoded, int nranks) : AllReduceOpHandle(node, local_scopes, places, ctxs), is_encoded_(is_encoded), nranks_(nranks) { @@ -102,7 +102,8 @@ void SparseAllReduceOpHandle::RunImplEncoded() { out_numel = (out_numel == 0) ? static_cast(out.numel()) : out_numel; int dev_id = boost::get(place).device; - auto &nccl_ctx = nccl_ctxs_->at(dev_id); + auto *nccl_ctxs = nccl_ctxs_->GetRunEnvNCCLCtx(run_order_, false); + auto &nccl_ctx = nccl_ctxs->at(dev_id); auto stream = nccl_ctx.stream(); auto comm = nccl_ctx.comm_; diff --git a/paddle/fluid/framework/details/sparse_all_reduce_op_handle.h b/paddle/fluid/framework/details/sparse_all_reduce_op_handle.h index ed6be65a2c8009fc417f4230b8169a4847e89440..9802f8dba7e05aec424f48d50992d065015179c9 100644 --- a/paddle/fluid/framework/details/sparse_all_reduce_op_handle.h +++ b/paddle/fluid/framework/details/sparse_all_reduce_op_handle.h @@ -32,7 +32,7 @@ class SparseAllReduceOpHandle : public AllReduceOpHandle { SparseAllReduceOpHandle(ir::Node *node, const std::vector &local_scopes, const std::vector &places, - const platform::NCCLContextMap *ctxs, + const platform::NCCLCommunicator *ctxs, bool is_encoded = false, int nranks = -1); std::string Name() const override; diff --git a/paddle/fluid/framework/details/ssa_graph_executor.cc b/paddle/fluid/framework/details/ssa_graph_executor.cc index af2cbd5c876fdd7c27cd679f7e9412d1b0604ecc..4f1e44ca26cb65468da6eded74653f34dbf00336 100644 --- a/paddle/fluid/framework/details/ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/ssa_graph_executor.cc @@ -19,10 +19,13 @@ namespace framework { namespace details { SSAGraphExecutor::~SSAGraphExecutor() {} -void ClearFetchOp(ir::Graph* graph, std::vector* fetch_ops) { +void ClearFetchOp(ir::Graph* graph, std::vector* fetch_ops) { if (fetch_ops->empty()) return; for (auto& op : *fetch_ops) { + PADDLE_ENFORCE_NOT_NULL( + dynamic_cast(op), + "The input ops of ClearFetchOp function should be FetchOpHandle."); for (auto& out_var : op->Node()->outputs) { graph->RemoveNode(out_var); } diff --git a/paddle/fluid/framework/details/ssa_graph_executor.h b/paddle/fluid/framework/details/ssa_graph_executor.h index 860eaa25b58e4579ad792ff18618de3b90707e8d..2454ec2b27d9d2060f28b8d6cea0ce49fe347433 100644 --- a/paddle/fluid/framework/details/ssa_graph_executor.h +++ b/paddle/fluid/framework/details/ssa_graph_executor.h @@ -38,7 +38,7 @@ class SSAGraphExecutor { virtual FeedFetchList Run(const std::vector& fetch_tensors) = 0; }; -void ClearFetchOp(ir::Graph* graph, std::vector* fetch_ops); +void ClearFetchOp(ir::Graph* graph, std::vector* fetch_ops); } // namespace details } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc index 67246a4dd448b0ce2f115d6438c5fdd6cc39ca6d..ac62f1dd83397a15830eae02c0ba00920a90dcfd 100644 --- a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc @@ -53,74 +53,84 @@ inline FeedFetchList ThreadedSSAGraphExecutor::RunImpl( new platform::RecordEvent("ThreadedSSAGraphExecutorPrepare")); std::unique_ptr op_deps = op_deps_futures_.get(); CopyOpDeps(); + VLOG(10) << "ThreadedSSAGraphExecutor::Run"; std::shared_ptr> ready_vars( new BlockingQueue); auto &pending_ops = op_deps->pending_ops_; auto &pending_vars = op_deps->pending_vars_; auto &ready_ops = op_deps->ready_ops_; - - // For ops (e.g. nccl_all_reduce) that need to coordinate multiple - // streams from multiple GPUs, it's faster to buffer them and schedule - // together since we currently cannot overlap computation and memcpy streams. - // Should revisit it if overlapping is available. - std::unordered_set delayed_ops; + size_t num_ops = op_deps->num_ops_; // Step 2. Insert FetchOps - std::vector fetch_ops; + std::vector fetch_ops; std::unordered_set fetch_dependencies; FeedFetchList fetch_data(fetch_tensors.size()); InsertFetchOps(fetch_tensors, &fetch_ops, &fetch_dependencies, &ready_ops, &pending_ops, &pending_vars, &fetch_data); - auto run_all_ops = [&](std::unordered_set &set) { - for (auto *op : set) { - RunOp(ready_vars, op); - } - set.clear(); - }; - // Clean run context - run_op_futures_.clear(); exception_holder_.Clear(); event.reset(nullptr); + // Step 3. Execution - while (!pending_vars.empty()) { - // 1. Run All Ready ops - // Keep loop until all vars are ready. - run_all_ops(ready_ops); - - // 2. Find ready variable - bool timeout; - auto cur_ready_vars = ready_vars->PopAll(1, &timeout); - if (timeout) { - if (exception_holder_.IsCaught()) { - VLOG(3) << "caught exception " << exception_holder_.Type() - << ", rethrow it"; + if (strategy_.num_threads_ == 1 && traced_ops_.size() == num_ops) { + // If the num_threads is 1, we can record the order of operator's + // execution in the first iteration, and in subsequent iterations, + // run the recorded operators directly. This strategy could make the + // execution faster. + VLOG(3) << "Run the traced ops."; + RunTracedOps(traced_ops_); + RunTracedOps(fetch_ops); + if (exception_holder_.IsCaught()) { + ExecutionFinal(&fetch_ops); + } + } else { + traced_ops_.clear(); + auto run_all_ops = [&](std::unordered_set &set) { + for (auto *op : set) { + RunOp(ready_vars, op); + } + set.clear(); + }; + // Clean run context + run_op_futures_.clear(); + + while (!pending_vars.empty()) { + // 1. Run All Ready ops + // Keep loop until all vars are ready. + run_all_ops(ready_ops); + + // 2. Find ready variable + bool timeout; + auto cur_ready_vars = ready_vars->PopAll(1, &timeout); + if (timeout) { for (auto &run_op_future : run_op_futures_) { run_op_future.wait(); } - ClearFetchOp(graph_, &fetch_ops); - exception_holder_.ReThrow(); - } else { - continue; + if (exception_holder_.IsCaught()) { + ExecutionFinal(&fetch_ops); + } else { + continue; + } } - } - // 3. Remove the dependency of ready_var. - // Find the ready_ops after the ready_var. - for (auto ready_var : cur_ready_vars) { - pending_vars.erase(ready_var); - for (auto *op : ready_var->PendingOps()) { - auto &deps = pending_ops[op]; - --deps; - if (deps == 0) { - ready_ops.insert(op); + // 3. Remove the dependency of ready_var. + // Find the ready_ops after the ready_var. + for (auto ready_var : cur_ready_vars) { + pending_vars.erase(ready_var); + for (auto *op : ready_var->PendingOps()) { + auto &deps = pending_ops[op]; + --deps; + if (deps == 0) { + ready_ops.insert(op); + } } } } + PADDLE_ENFORCE(ready_ops.empty()); } - PADDLE_ENFORCE(ready_ops.empty()); + // Wait FetchOps. ClearFetchOp(graph_, &fetch_ops); @@ -137,7 +147,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( void ThreadedSSAGraphExecutor::InsertFetchOps( const std::vector &fetch_tensors, - std::vector *fetch_ops, + std::vector *fetch_ops, std::unordered_set *fetch_dependencies, std::unordered_set *ready_ops, std::unordered_map *pending_ops, @@ -243,6 +253,9 @@ void ThreadedSSAGraphExecutor::PrepareOpDeps() { InsertPendingOp(&pending_ops, op); } } + op_deps_->num_ops_ = ready_ops.size() + pending_ops.size(); + PADDLE_ENFORCE_GT(op_deps_->num_ops_, 0, "The graph doesn't have operators."); + for (auto ready_var : ready_vars) { pending_vars.erase(ready_var); for (auto *op : ready_var->PendingOps()) { @@ -264,6 +277,7 @@ void ThreadedSSAGraphExecutor::CopyOpDeps() { op_deps_->pending_vars_.end()); op_deps->ready_ops_.insert(op_deps_->ready_ops_.begin(), op_deps_->ready_ops_.end()); + op_deps->num_ops_ = op_deps_->num_ops_; return std::unique_ptr(op_deps); }); } @@ -272,25 +286,59 @@ void ThreadedSSAGraphExecutor::RunOp( const std::shared_ptr> &ready_var_q, details::OpHandleBase *op) { auto op_run = [ready_var_q, op, this] { + RunOpSync(op); try { - if (VLOG_IS_ON(10)) { - VLOG(10) << op << " " << op->Name() << " : " << op->DebugString(); - } - if (LIKELY(!strategy_.dry_run_)) { - op->Run(strategy_.use_cuda_); - } - VLOG(10) << op << " " << op->Name() << " Done "; ready_var_q->Extend(op->Outputs()); VLOG(10) << op << " " << op->Name() << " Signal posted"; } catch (...) { exception_holder_.Catch(std::current_exception()); } }; + if (pool_) { run_op_futures_.emplace_back(pool_->enqueue(op_run)); } else { op_run(); } + + RecordOps(op); +} + +void ThreadedSSAGraphExecutor::RunTracedOps( + const std::vector &traced_ops) { + for (auto &op : traced_ops) { + if (exception_holder_.IsCaught()) { + return; + } + RunOpSync(op); + } +} + +void ThreadedSSAGraphExecutor::RunOpSync(OpHandleBase *op) { + try { + if (VLOG_IS_ON(10)) { + VLOG(10) << op << " " << op->Name() << " : " << op->DebugString(); + } + if (LIKELY(!strategy_.dry_run_)) { + op->Run(strategy_.use_cuda_); + } + VLOG(10) << op << " " << op->Name() << " Done "; + } catch (...) { + exception_holder_.Catch(std::current_exception()); + } +} + +void ThreadedSSAGraphExecutor::ExecutionFinal( + std::vector *fetch_ops) { + VLOG(3) << "caught exception " << exception_holder_.Type() << ", rethrow it"; + ClearFetchOp(graph_, fetch_ops); + exception_holder_.ReThrow(); +} + +void ThreadedSSAGraphExecutor::RecordOps(OpHandleBase *op) { + if (strategy_.num_threads_ == 1 && !dynamic_cast(op)) { + traced_ops_.emplace_back(op); + } } } // namespace details } // namespace framework diff --git a/paddle/fluid/framework/details/threaded_ssa_graph_executor.h b/paddle/fluid/framework/details/threaded_ssa_graph_executor.h index 8c026057b480fbc40b7b8f12d8e6b8e54195a141..6c1fb1c6c0a7b55cee89986c00bf650542520355 100644 --- a/paddle/fluid/framework/details/threaded_ssa_graph_executor.h +++ b/paddle/fluid/framework/details/threaded_ssa_graph_executor.h @@ -44,6 +44,7 @@ struct OpDependentData { std::unordered_map pending_ops_; std::unordered_set pending_vars_; std::unordered_set ready_ops_; + size_t num_ops_{0}; }; class ThreadedSSAGraphExecutor : public SSAGraphExecutor { @@ -80,6 +81,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor { std::list> run_op_futures_; ::ThreadPool prepare_pool_; std::unique_ptr<::ThreadPool> pool_; + std::vector traced_ops_; void InsertPendingOp(std::unordered_map *pending_ops, OpHandleBase *op_instance) const; @@ -89,7 +91,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor { VarHandleBase *var) const; void InsertFetchOps(const std::vector &fetch_tensors, - std::vector *fetch_ops, + std::vector *fetch_ops, std::unordered_set *fetch_dependencies, std::unordered_set *ready_ops, std::unordered_map *pending_ops, @@ -97,7 +99,16 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor { FeedFetchList *fetch_data); void PrepareOpDeps(); + void CopyOpDeps(); + + inline void RecordOps(OpHandleBase *op); + + inline void ExecutionFinal(std::vector *fetch_ops); + + inline void RunOpSync(OpHandleBase *op); + + void RunTracedOps(const std::vector &traced_ops); }; } // namespace details diff --git a/paddle/fluid/framework/device_worker.h b/paddle/fluid/framework/device_worker.h index a7a8663ec3b1c436104f53b6db833bd26f6722f0..be5f663e1c96c5500093f3cceb2716a185224a1d 100644 --- a/paddle/fluid/framework/device_worker.h +++ b/paddle/fluid/framework/device_worker.h @@ -14,6 +14,7 @@ limitations under the License. */ #pragma once +#include #include #include #include @@ -35,9 +36,17 @@ limitations under the License. */ #include "paddle/fluid/platform/port.h" #include "paddle/fluid/platform/timer.h" +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +#include "paddle/fluid/platform/nccl_helper.h" +#endif + namespace paddle { namespace framework { +#define SEC_LOG \ + VLOG(3) << "[s" << section_id_ << "p" << pipeline_id_ << "t" << thread_id_ \ + << "]: " + class PullDenseWorker { public: virtual ~PullDenseWorker() {} @@ -48,6 +57,7 @@ class PullDenseWorker { void IncreaseThreadVersion(int thread_id, uint64_t table_id); void ResetThreadVersion(uint64_t table_id); void Wait(std::vector<::std::future>* status_vec); + void PullDense(bool force_update = false); static std::shared_ptr GetInstance() { if (NULL == s_instance_) { s_instance_.reset(new paddle::framework::PullDenseWorker()); @@ -92,7 +102,7 @@ class PullDenseWorker { // should incorporate different type of device class DeviceWorker { public: - DeviceWorker() {} + DeviceWorker() { use_cvm_ = false; } virtual ~DeviceWorker() {} virtual void Initialize(const TrainerDesc& desc) = 0; virtual void SetDeviceIndex(int tid) = 0; @@ -114,6 +124,7 @@ class DeviceWorker { std::shared_ptr device_reader_; int64_t batch_num_; FetchConfig fetch_config_; + bool use_cvm_; }; class CPUWorkerBase : public DeviceWorker { @@ -194,5 +205,101 @@ class DownpourWorker : public HogwildWorker { std::vector<::std::future> push_dense_status_; }; +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +using ScopeQueue = operators::reader::BlockingQueue; + +class SyncFunctor { + public: + SyncFunctor(int rank_id, int rank_num, int sync_steps); + virtual ~SyncFunctor() {} + + void SetSyncParam(const std::vector& sync_param) { + sync_param_ = &sync_param; + } + void SetNcclCtxMap(platform::NCCLContextMap* nccl_ctx_map) { + nccl_ctx_map_ = nccl_ctx_map; + } + + int operator()(Scope* scope); + static std::vector pipeline_scopes_; + static uint64_t sync_flag_; + + protected: + const int rank_id_; + const int rank_num_; + const std::vector* sync_param_ = nullptr; + platform::NCCLContextMap* nccl_ctx_map_ = nullptr; + + uint64_t sync_signal_; + const int sync_steps_; + int counter_; + + void Synchronize(); +}; + +class SectionWorker : public DeviceWorker { + public: + SectionWorker() {} + ~SectionWorker() override {} + + void Initialize(const TrainerDesc& desc) override; + + void BindingDataFeedMemory() override {} + void CreateDeviceResource(const ProgramDesc& main_prog) override{}; + + void TrainFiles() override; + void TrainFilesWithProfiler() override; + + void PrintFetchVars() override {} + + const platform::Place& place() const { return place_; } + + void SetSectionIndex(int section_id) { section_id_ = section_id; } + void SetDeviceIndex(int tid) override { pipeline_id_ = tid; } + void SetThreadIndex(int thread_id) { thread_id_ = thread_id; } + void SetVarNames(const std::vector& in_var_names, + const std::vector& out_var_names) { + in_var_names_ = &in_var_names; + out_var_names_ = &out_var_names; + } + void SetScopeQueue(ScopeQueue* in_scope_queue, ScopeQueue* out_scope_queue) { + in_scope_queue_ = in_scope_queue; + out_scope_queue_ = out_scope_queue; + } + void SetCountMutex(std::mutex* mutex) { worker_count_mutex_ = mutex; } + void SetWorkerCount(int* worker_count) { worker_count_ = worker_count; } + void SetSectionNum(int section_num) { section_num_ = section_num; } + void SetPipelineNum(int pipeline_num) { pipeline_num_ = pipeline_num; } + void SetNextSectionPlace(const paddle::platform::Place& place) { + next_section_place_ = place; + } + SyncFunctor* sync_func_ = nullptr; + void SetSyncFunctor(SyncFunctor* sync_func) { sync_func_ = sync_func; } + + static std::atomic cpu_id_; + + protected: + void AutoSetCPUAffinity(bool reuse); + int section_id_; + int pipeline_id_; + int section_num_; + int pipeline_num_; + int thread_id_; + + // This worker will consume scope from in_scope_queue_ + // and produce scope to out_scope_queue_ + ScopeQueue* in_scope_queue_ = nullptr; + ScopeQueue* out_scope_queue_ = nullptr; + const std::vector* in_var_names_ = nullptr; + const std::vector* out_var_names_ = nullptr; + std::mutex* worker_count_mutex_ = nullptr; + int* worker_count_ = nullptr; + paddle::platform::Place next_section_place_; + + std::vector> ops_; + + platform::DeviceContext* dev_ctx_ = nullptr; +}; +#endif } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/device_worker_factory.cc b/paddle/fluid/framework/device_worker_factory.cc index 2a7b368145c3b16873fc90a34fe5bb439d9806dd..dc85941f57d172b79c06f5ab91933fe0fa50465e 100644 --- a/paddle/fluid/framework/device_worker_factory.cc +++ b/paddle/fluid/framework/device_worker_factory.cc @@ -61,5 +61,8 @@ std::shared_ptr DeviceWorkerFactory::CreateDeviceWorker( REGISTER_DEVICE_WORKER_CLASS(HogwildWorker); REGISTER_DEVICE_WORKER_CLASS(DownpourWorker); +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +REGISTER_DEVICE_WORKER_CLASS(SectionWorker); +#endif } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/downpour_worker.cc b/paddle/fluid/framework/downpour_worker.cc index 8e184e5d3cbc6d73c45aef97981dda410bc0f962..0b4e959f571c4d2d9482a00647874400e2d7793b 100644 --- a/paddle/fluid/framework/downpour_worker.cc +++ b/paddle/fluid/framework/downpour_worker.cc @@ -63,6 +63,7 @@ void DownpourWorker::Initialize(const TrainerDesc& desc) { fleet_ptr_ = FleetWrapper::GetInstance(); fetch_config_ = desc.fetch_config(); + use_cvm_ = desc.use_cvm(); } void DownpourWorker::CollectLabelInfo(size_t table_idx) { @@ -139,14 +140,25 @@ void DownpourWorker::FillSparseValue(size_t table_idx) { LoD data_lod{tensor_lod}; tensor_emb->set_lod(data_lod); for (int index = 0; index < len; ++index) { - if (ids[index] == 0u) { - memcpy(ptr + table.emb_dim() * index, init_value.data() + 2, + if (use_cvm_) { + if (ids[index] == 0u) { + memcpy(ptr + table.emb_dim() * index, init_value.data(), + sizeof(float) * table.emb_dim()); + continue; + } + memcpy(ptr + table.emb_dim() * index, fea_value[fea_idx].data(), + sizeof(float) * table.emb_dim()); + fea_idx++; + } else { + if (ids[index] == 0u) { + memcpy(ptr + table.emb_dim() * index, init_value.data() + 2, + sizeof(float) * table.emb_dim()); + continue; + } + memcpy(ptr + table.emb_dim() * index, fea_value[fea_idx].data() + 2, sizeof(float) * table.emb_dim()); - continue; + fea_idx++; } - memcpy(ptr + table.emb_dim() * index, fea_value[fea_idx].data() + 2, - sizeof(float) * table.emb_dim()); - fea_idx++; } } } @@ -197,9 +209,9 @@ void DownpourWorker::TrainFilesWithProfiler() { uint64_t tid = static_cast( param_.program_config(0).pull_sparse_table_id(i)); TableParameter table; - for (auto i : param_.sparse_table()) { - if (i.table_id() == tid) { - table = i; + for (auto j : param_.sparse_table()) { + if (j.table_id() == tid) { + table = j; break; } } @@ -259,7 +271,7 @@ void DownpourWorker::TrainFilesWithProfiler() { fleet_ptr_->PushSparseVarsWithLabelAsync( *thread_scope_, tid, features_[tid], feature_labels_[tid], sparse_key_names_[tid], sparse_grad_names_[tid], table.emb_dim(), - &feature_grads_[tid], &push_sparse_status_); + &feature_grads_[tid], &push_sparse_status_, cur_batch, use_cvm_); timeline.Pause(); push_sparse_time += timeline.ElapsedSec(); total_time += timeline.ElapsedSec(); @@ -367,9 +379,9 @@ void DownpourWorker::TrainFiles() { uint64_t tid = static_cast( param_.program_config(0).pull_sparse_table_id(i)); TableParameter table; - for (auto i : param_.sparse_table()) { - if (i.table_id() == tid) { - table = i; + for (auto j : param_.sparse_table()) { + if (j.table_id() == tid) { + table = j; break; } } @@ -411,7 +423,7 @@ void DownpourWorker::TrainFiles() { fleet_ptr_->PushSparseVarsWithLabelAsync( *thread_scope_, tid, features_[tid], feature_labels_[tid], sparse_key_names_[tid], sparse_grad_names_[tid], table.emb_dim(), - &feature_grads_[tid], &push_sparse_status_); + &feature_grads_[tid], &push_sparse_status_, cur_batch, use_cvm_); } } diff --git a/paddle/fluid/framework/executor.cc b/paddle/fluid/framework/executor.cc index 239a3ce0a84e9d0f4b3395bdbbd3fdae58e8b36a..e36871e8d825623b8b14046a1a73d1705d63933f 100644 --- a/paddle/fluid/framework/executor.cc +++ b/paddle/fluid/framework/executor.cc @@ -122,8 +122,9 @@ void Executor::RunFromDataset(const ProgramDesc& main_program, Scope* scope, const std::string& trainer_desc_str) { VLOG(3) << "Start to RunFromDataset in executor"; TrainerDesc trainer_desc; - google::protobuf::TextFormat::ParseFromString(trainer_desc_str, - &trainer_desc); + bool success = trainer_desc.ParseFromString(trainer_desc_str); + PADDLE_ENFORCE(success, "Fail to parse TrainerDesc from string:\n%s", + trainer_desc_str.c_str()); VLOG(3) << "Going to create trainer, trainer class is " << trainer_desc.class_name(); std::shared_ptr trainer; @@ -244,6 +245,12 @@ static bool has_fetch_operators( return fetch_count > 0; } +std::unique_ptr Executor::PrepareCtxCache( + const ProgramDesc& program, int block_id, + const std::vector& skip_ref_cnt_vars, bool force_disable_gc) { + return Prepare(program, block_id, skip_ref_cnt_vars, force_disable_gc); +} + void Executor::Run(const ProgramDesc& program, Scope* scope, std::map* feed_targets, std::map* fetch_targets, @@ -328,7 +335,7 @@ std::unique_ptr Executor::Prepare( ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc)); } #ifdef PADDLE_WITH_NGRAPH - if (FLAGS_use_ngraph) { + if (FLAGS_use_ngraph && ctx->block_id_ == 0) { paddle::operators::NgraphEngine::FuseNgraphOps( ctx->prog_.Block(ctx->block_id_), &ctx->ops_); } @@ -368,6 +375,7 @@ std::vector> Executor::Prepare( void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, bool create_local_scope, bool create_vars, bool keep_kids) { + platform::RecordBlock b(kProgramId); PADDLE_ENFORCE_NOT_NULL(scope); Scope* local_scope = scope; if (create_vars) { @@ -407,7 +415,6 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, for (auto& op : ctx->ops_) { op->Run(*local_scope, place_); - if (gc) { DeleteUnusedTensors(*local_scope, op.get(), ctx->unused_vars_, gc.get()); } diff --git a/paddle/fluid/framework/executor.h b/paddle/fluid/framework/executor.h index 6eeeb1efc6117f341026097359199cc26554649d..d0d12b307205a76fbf4669ac060223329b41b533 100644 --- a/paddle/fluid/framework/executor.h +++ b/paddle/fluid/framework/executor.h @@ -83,6 +83,21 @@ class Executor { const std::string& feed_holder_name = "feed", const std::string& fetch_holder_name = "fetch"); + // This API is very slow. + void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, + std::map* feed_targets, + std::map* fetch_targets, + bool create_local_scope = true, + bool create_vars = true, + const std::string& feed_holder_name = "feed", + const std::string& fetch_holder_name = "fetch"); + + std::unique_ptr PrepareCtxCache( + const ProgramDesc& program, int block_id, + const std::vector& skip_ref_cnt_vars = + std::vector(), + bool force_disable_gc = false); + static std::unique_ptr Prepare( const ProgramDesc& program, int block_id, const std::vector& skip_ref_cnt_vars = @@ -101,15 +116,6 @@ class Executor { bool create_local_scope = true, bool create_vars = true, bool keep_kids = false); - // This API is very slow. - void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, - std::map* feed_targets, - std::map* fetch_targets, - bool create_local_scope = true, - bool create_vars = true, - const std::string& feed_holder_name = "feed", - const std::string& fetch_holder_name = "fetch"); - void EnableMKLDNN(const ProgramDesc& program); void RunFromDataset(const ProgramDesc& main_program, Scope* scope, diff --git a/paddle/fluid/framework/fleet/fleet_wrapper.cc b/paddle/fluid/framework/fleet/fleet_wrapper.cc index 394ff24c466622956b18b3012c146f6f9ddd838e..fd77cdeb7cb7366a169d956f4cf44cb71e22f87f 100644 --- a/paddle/fluid/framework/fleet/fleet_wrapper.cc +++ b/paddle/fluid/framework/fleet/fleet_wrapper.cc @@ -281,9 +281,16 @@ void FleetWrapper::PushSparseVarsWithLabelAsync( const std::vector& sparse_key_names, const std::vector& sparse_grad_names, const int emb_dim, std::vector>* push_values, - std::vector<::std::future>* push_sparse_status) { + std::vector<::std::future>* push_sparse_status, + const int batch_size, const bool use_cvm) { #ifdef PADDLE_WITH_PSLIB int offset = 2; + int grad_dim = emb_dim; + if (use_cvm) { + offset = 0; + grad_dim = emb_dim - 2; + } + CHECK_GE(grad_dim, 0); uint64_t fea_idx = 0u; for (size_t i = 0; i < sparse_key_names.size(); ++i) { Variable* g_var = scope.FindVar(sparse_grad_names[i]); @@ -307,7 +314,13 @@ void FleetWrapper::PushSparseVarsWithLabelAsync( for (auto& t : *push_values) { t.resize(emb_dim + offset); } - + if (scale_sparse_gradient_with_batch_size_ && grad_dim > 0) { + int dim = emb_dim + offset; + Eigen::Map< + Eigen::Matrix> + g_mat(g, g_tensor->numel() / dim, dim); + g_mat.rightCols(grad_dim) *= batch_size; + } for (auto id_idx = 0u; id_idx < len; ++id_idx) { if (ids[id_idx] == 0) { g += emb_dim; @@ -315,10 +328,15 @@ void FleetWrapper::PushSparseVarsWithLabelAsync( } CHECK(fea_idx < (*push_values).size()); CHECK(fea_idx < fea_labels.size()); - memcpy((*push_values)[fea_idx].data() + offset, g, - sizeof(float) * emb_dim); - (*push_values)[fea_idx][0] = 1.0f; - (*push_values)[fea_idx][1] = static_cast(fea_labels[fea_idx]); + if (use_cvm) { + memcpy((*push_values)[fea_idx].data() + offset, g, + sizeof(float) * emb_dim); + } else { + memcpy((*push_values)[fea_idx].data() + offset, g, + sizeof(float) * emb_dim); + (*push_values)[fea_idx][0] = 1.0f; + (*push_values)[fea_idx][1] = static_cast(fea_labels[fea_idx]); + } g += emb_dim; fea_idx++; } @@ -337,6 +355,89 @@ void FleetWrapper::PushSparseVarsWithLabelAsync( #endif } +void FleetWrapper::LoadModel(const std::string& path, const int mode) { +#ifdef PADDLE_WITH_PSLIB + auto ret = pslib_ptr_->_worker_ptr->load(path, std::to_string(mode)); + ret.wait(); + if (ret.get() != 0) { + LOG(ERROR) << "load model from path:" << path << " failed"; + exit(-1); + } +#else + VLOG(0) << "FleetWrapper::LoadModel does nothing when no pslib"; +#endif +} + +void FleetWrapper::SaveModel(const std::string& path, const int mode) { +#ifdef PADDLE_WITH_PSLIB + auto ret = pslib_ptr_->_worker_ptr->save(path, std::to_string(mode)); + ret.wait(); + int32_t feasign_cnt = ret.get(); + if (feasign_cnt == -1) { + LOG(ERROR) << "save model failed"; + exit(-1); + } +#else + VLOG(0) << "FleetWrapper::SaveModel does nothing when no pslib"; +#endif +} + +void FleetWrapper::ShrinkSparseTable(int table_id) { +#ifdef PADDLE_WITH_PSLIB + auto ret = pslib_ptr_->_worker_ptr->shrink(table_id); + ret.wait(); +#else + VLOG(0) << "FleetWrapper::ShrinkSparseTable does nothing when no pslib"; +#endif +} + +void FleetWrapper::ShrinkDenseTable(int table_id, Scope* scope, + std::vector var_list, + float decay) { +#ifdef PADDLE_WITH_PSLIB + std::vector regions; + for (std::string& name : var_list) { + if (name.find("batch_sum") != std::string::npos) { + Variable* var = scope->FindVar(name); + CHECK(var != nullptr) << "var[" << name << "] not found"; + VLOG(3) << "prepare shrink dense batch_sum"; + LoDTensor* tensor = var->GetMutable(); + float* g = tensor->data(); + Eigen::Map mat(g, 1, tensor->numel()); + mat *= decay; + paddle::ps::Region reg(g, tensor->numel()); + regions.emplace_back(std::move(reg)); + } else { + Variable* var = scope->FindVar(name); + CHECK(var != nullptr) << "var[" << name << "] not found"; + LoDTensor* tensor = var->GetMutable(); + float* g = tensor->data(); + paddle::ps::Region reg(g, tensor->numel()); + regions.emplace_back(std::move(reg)); + } + } + auto push_status = pslib_ptr_->_worker_ptr->push_dense_param( + regions.data(), regions.size(), table_id); + push_status.wait(); + auto status = push_status.get(); + if (status != 0) { + LOG(FATAL) << "push shrink dense param failed, status[" << status << "]"; + exit(-1); + } +#else + VLOG(0) << "FleetWrapper::ShrinkSparseTable does nothing when no pslib"; +#endif +} + +void FleetWrapper::ClientFlush() { +#ifdef PADDLE_WITH_PSLIB + auto ret = pslib_ptr_->_worker_ptr->flush(); + ret.wait(); +#else + VLOG(0) << "FleetWrapper::ServerFlush does nothing when no pslib"; +#endif +} + int FleetWrapper::RegisterClientToClientMsgHandler(int msg_type, MsgHandlerFunc handler) { #ifdef PADDLE_WITH_PSLIB @@ -398,6 +499,24 @@ void FleetWrapper::Deserialize(std::vector* t, const std::string& str) { #endif } +std::default_random_engine& FleetWrapper::LocalRandomEngine() { + struct engine_wrapper_t { + std::default_random_engine engine; +#ifdef PADDLE_WITH_PSLIB + engine_wrapper_t() { + struct timespec tp; + clock_gettime(CLOCK_REALTIME, &tp); + double cur_time = tp.tv_sec + tp.tv_nsec * 1e-9; + static std::atomic x(0); + std::seed_seq sseq = {x++, x++, x++, (uint64_t)(cur_time * 1000)}; + engine.seed(sseq); + } +#endif + }; + thread_local engine_wrapper_t r; + return r.engine; +} + template void FleetWrapper::Serialize>( const std::vector*>&, std::string*); template void FleetWrapper::Deserialize>( diff --git a/paddle/fluid/framework/fleet/fleet_wrapper.h b/paddle/fluid/framework/fleet/fleet_wrapper.h index 386e711ff71dbf978cbcb620589490d3f06d3c53..b62270a488e79c08ae897797525bc1f7cf24c9c3 100644 --- a/paddle/fluid/framework/fleet/fleet_wrapper.h +++ b/paddle/fluid/framework/fleet/fleet_wrapper.h @@ -55,7 +55,7 @@ namespace framework { class FleetWrapper { public: virtual ~FleetWrapper() {} - FleetWrapper() {} + FleetWrapper() { scale_sparse_gradient_with_batch_size_ = true; } // Pull sparse variables from server in Sync mode // Param: scope, table_id, var_names, fea_keys // Param: fea_values @@ -99,7 +99,8 @@ class FleetWrapper { const std::vector& sparse_key_names, const std::vector& sparse_grad_names, const int emb_dim, std::vector>* push_values, - std::vector<::std::future>* push_sparse_status); + std::vector<::std::future>* push_sparse_status, + const int batch_size, const bool use_cvm); // Push sparse variables to server in Async mode // Param: scope, table_id, fea_keys, sparse_grad_names @@ -128,6 +129,19 @@ class FleetWrapper { // create client to client connection void CreateClient2ClientConnection(); + // flush all push requests + void ClientFlush(); + // mode = 0, load all feature + // mode = 1, laod delta feature, which means load diff + void LoadModel(const std::string& path, const int mode); + // mode = 0, save all feature + // mode = 1, save delta feature, which means save diff + void SaveModel(const std::string& path, const int mode); + + void ShrinkSparseTable(int table_id); + void ShrinkDenseTable(int table_id, Scope* scope, + std::vector var_list, float decay); + // register client to client communication typedef std::function MsgHandlerFunc; int RegisterClientToClientMsgHandler(int msg_type, MsgHandlerFunc handler); @@ -146,6 +160,9 @@ class FleetWrapper { return s_instance_; } + // this performs better than rand_r, especially large data + std::default_random_engine& LocalRandomEngine(); + #ifdef PADDLE_WITH_PSLIB static std::shared_ptr pslib_ptr_; #endif @@ -158,6 +175,7 @@ class FleetWrapper { protected: static bool is_initialized_; + bool scale_sparse_gradient_with_batch_size_; DISABLE_COPY_AND_ASSIGN(FleetWrapper); }; diff --git a/paddle/fluid/framework/framework.proto b/paddle/fluid/framework/framework.proto index 6c60a041a191f1db4a755c1c5714724342053791..efdabffb9b33ddf007c13008d0f3afb7a3961eda 100644 --- a/paddle/fluid/framework/framework.proto +++ b/paddle/fluid/framework/framework.proto @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ syntax = "proto2"; -// option optimize_for = LITE_RUNTIME; +option optimize_for = LITE_RUNTIME; package paddle.framework.proto; // Any incompatible changes to ProgramDesc and its dependencies should diff --git a/paddle/fluid/framework/hogwild_worker.cc b/paddle/fluid/framework/hogwild_worker.cc index 75c985d10f3b24cc1a49f2e6f87a89550f170c5d..f02828ebaee863e9e2b9aba7a925c103c2a9e9e4 100644 --- a/paddle/fluid/framework/hogwild_worker.cc +++ b/paddle/fluid/framework/hogwild_worker.cc @@ -24,9 +24,10 @@ void HogwildWorker::Initialize(const TrainerDesc& desc) { fetch_config_ = desc.fetch_config(); param_ = desc.hogwild_param(); skip_ops_.resize(param_.skip_ops_size()); - for (size_t i = 0; i < param_.skip_ops_size(); ++i) { + for (int i = 0; i < param_.skip_ops_size(); ++i) { skip_ops_[i] = param_.skip_ops(i); } + use_cvm_ = desc.use_cvm(); } void HogwildWorker::CreateThreadOperators(const ProgramDesc& program) { diff --git a/paddle/fluid/framework/ir/CMakeLists.txt b/paddle/fluid/framework/ir/CMakeLists.txt index 032fcbedf49cb96c93e85971d5c03915af044310..5228840c960aca4817f03f6421a24139054e5575 100644 --- a/paddle/fluid/framework/ir/CMakeLists.txt +++ b/paddle/fluid/framework/ir/CMakeLists.txt @@ -72,12 +72,12 @@ pass_library(transpose_flatten_concat_fuse_pass inference) pass_library(identity_scale_op_clean_pass base) pass_library(sync_batch_norm_pass base) pass_library(runtime_context_cache_pass base) -pass_library(expected_kernel_cache_pass base) pass_library(quant_conv2d_dequant_fuse_pass inference) pass_library(fillconstant_elementwisemul_fuse inference) pass_library(shuffle_channel_detect_pass inference) +pass_library(delete_quant_dequant_op_pass inference) -if(ANAKIN_FOUND) +if(ANAKIN_SUBGRAPH) pass_library(simplify_anakin_priorbox_detection_out_pass inference) endif() @@ -86,12 +86,23 @@ if(WITH_MKLDNN) pass_library(depthwise_conv_mkldnn_pass base mkldnn) pass_library(conv_bias_mkldnn_fuse_pass inference mkldnn) pass_library(conv_relu_mkldnn_fuse_pass inference mkldnn) + pass_library(conv_brelu_mkldnn_fuse_pass inference mkldnn) + pass_library(conv_concat_relu_mkldnn_fuse_pass inference mkldnn) pass_library(conv_elementwise_add_mkldnn_fuse_pass inference mkldnn) + pass_library(fc_mkldnn_pass inference mkldnn) pass_library(cpu_quantize_placement_pass base mkldnn) pass_library(cpu_quantize_pass inference mkldnn) pass_library(cpu_quantize_squash_pass inference mkldnn) endif() +if(WITH_NGRAPH) + cc_library(ngraph_subgraph_pass SRCS ngraph_subgraph_pass.cc DEPS ngraph_bridge + analysis_helper subgraph_detector graph_pattern_detector pass fuse_pass_base ${op_library_DEPS}) + set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h) + file(APPEND ${pass_file} "USE_PASS(ngraph_subgraph_pass);\n") + set(INFER_IR_PASSES ${INFER_IR_PASSES} ngraph_subgraph_pass CACHE INTERNAL "") +endif() + cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector ) cc_library(fuse_relu_depthwise_conv_pass SRCS fuse_relu_depthwise_conv_pass.cc DEPS pass graph_pattern_detector ) @@ -115,6 +126,8 @@ if (WITH_MKLDNN) cc_test(test_depthwise_conv_mkldnn_pass SRCS mkldnn/depthwise_conv_mkldnn_pass_tester.cc DEPS depthwise_conv_mkldnn_pass) cc_test(test_conv_bias_mkldnn_fuse_pass SRCS mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc DEPS conv_bias_mkldnn_fuse_pass naive_executor) cc_test(test_conv_relu_mkldnn_fuse_pass SRCS mkldnn/conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass) + cc_test(test_conv_brelu_mkldnn_fuse_pass SRCS mkldnn/conv_brelu_mkldnn_fuse_pass_tester.cc DEPS conv_brelu_mkldnn_fuse_pass) + cc_test(test_conv_concat_relu_mkldnn_fuse_pass SRCS mkldnn/conv_concat_relu_mkldnn_fuse_pass_tester.cc DEPS conv_concat_relu_mkldnn_fuse_pass) cc_test(test_conv_elementwise_add_mkldnn_fuse_pass SRCS mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS conv_elementwise_add_mkldnn_fuse_pass) cc_test(test_mkldnn_placement_pass SRCS mkldnn/mkldnn_placement_pass_tester.cc DEPS mkldnn_placement_pass) cc_test(test_cpu_quantize_placement_pass SRCS mkldnn/cpu_quantize_placement_pass_tester.cc DEPS cpu_quantize_placement_pass) diff --git a/paddle/fluid/framework/ir/alloc_continuous_space_for_grad_pass.cc b/paddle/fluid/framework/ir/alloc_continuous_space_for_grad_pass.cc index 12d5ad7ed8ccbe3db925ce59dacf935dad158e5c..715ca97f3715128c6d2ccfcbb8d291f84f176a6d 100644 --- a/paddle/fluid/framework/ir/alloc_continuous_space_for_grad_pass.cc +++ b/paddle/fluid/framework/ir/alloc_continuous_space_for_grad_pass.cc @@ -23,15 +23,16 @@ #include "paddle/fluid/framework/ir/graph_helper.h" #include "paddle/fluid/framework/op_registry.h" -DEFINE_uint64(fuse_parameter_memory_size, 0, // 0 KB - "fuse_parameter_memory_size is up limited memory size " +DEFINE_double(fuse_parameter_memory_size, -1.0, // MBytes + "fuse_parameter_memory_size is up limited memory size(MB)" "of one group parameters' gradient which is the input " "of communication calling(e.g NCCLAllReduce). " "The default value is 0, it means that " "not set group according to memory_size."); DEFINE_int32( - fuse_parameter_groups_size, 3, - "fuse_parameter_groups_size is the size of one group parameters' gradient. " + fuse_parameter_groups_size, 1, + "fuse_parameter_groups_size is the up limited size of one group " + "parameters' gradient. " "The default value is a experimental result. If the " "fuse_parameter_groups_size is 1, it means that the groups size is " "the number of parameters' gradient. If the fuse_parameter_groups_size is " @@ -41,6 +42,9 @@ DEFINE_int32( namespace paddle { namespace framework { namespace ir { +// unit of the FLAGS_fuse_parameter_memory_size. +static constexpr double kMB = 1048576.0; + // SetFuseParameterGroupsSize and SetFuseParameterMemorySize are used in unit // test, because it is invalid that seting 'FLAGS_fuse_parameter_memory_size' // and 'FLAGS_fuse_parameter_groups_size' in unit test. @@ -50,15 +54,12 @@ void SetFuseParameterGroupsSize(int group_size) { int GetFuseParameterGroupsSize() { return FLAGS_fuse_parameter_groups_size; } -void SetFuseParameterMemorySize(uint64_t memory_size) { +void SetFuseParameterMemorySize(double memory_size) { FLAGS_fuse_parameter_memory_size = memory_size; } -uint64_t GetFuseParameterMemorySize() { - return FLAGS_fuse_parameter_memory_size; -} +double GetFuseParameterMemorySize() { return FLAGS_fuse_parameter_memory_size; } -static const char kUnKnow[] = "@UNKNOW@"; static framework::proto::VarType::Type kDefaultDtype = framework::proto::VarType::Type::VarType_Type_BOOL; @@ -83,7 +84,7 @@ class AllocContinuousSpaceForGradPass : public ir::Pass { } if (params_grads.size() == 0) { - VLOG(10) << "Doesn't find gradients"; + LOG(WARNING) << "Doesn't find gradients"; return; } @@ -169,7 +170,6 @@ class AllocContinuousSpaceForGradPass : public ir::Pass { details::GroupGradsAndParams *group_grads_params) const { SetGroupAccordingToLayers(var_nodes, params_grads, group_grads_params); SetGroupAccordingToMemorySize(var_nodes, group_grads_params); - SetGroupAccordingToGroupSize(var_nodes, group_grads_params); } void SetGroupAccordingToLayers( @@ -181,7 +181,7 @@ class AllocContinuousSpaceForGradPass : public ir::Pass { for (size_t i = 0; i < params_grads.size(); ++i) { auto pos = params_grads[i].first.find_first_of("."); if (pos == std::string::npos) { - layer_params[std::string(kUnKnow)].emplace_back(i); + layer_params[params_grads[i].first].emplace_back(i); } else { layer_params[params_grads[i].first.substr(0, pos)].emplace_back(i); } @@ -190,7 +190,7 @@ class AllocContinuousSpaceForGradPass : public ir::Pass { group_grads_params->reserve(layer_params.size()); for (size_t i = 0; i < params_grads.size(); ++i) { auto pos = params_grads[i].first.find_first_of("."); - std::string key = kUnKnow; + std::string key = params_grads[i].first; if (pos != std::string::npos) { key = params_grads[i].first.substr(0, pos); } @@ -207,21 +207,40 @@ class AllocContinuousSpaceForGradPass : public ir::Pass { } VLOG(10) << "SetGroupAccordingToLayers: "; + if (VLOG_IS_ON(10)) { + PrintGroupInfo(var_nodes, group_grads_params); + } + } + + void PrintGroupInfo( + const std::unordered_map &var_nodes, + details::GroupGradsAndParams *group_grads_params) const { for (size_t i = 0; i < group_grads_params->size(); ++i) { VLOG(10) << "group " << i; std::stringstream out; - for (auto &p_g : group_grads_params->at(i)) { - out << "(" << p_g.second << ", " << p_g.first << "), "; + size_t gps_size = 0; + for (auto &g_p : group_grads_params->at(i)) { + auto iter = var_nodes.find(g_p.second); + PADDLE_ENFORCE(iter != var_nodes.end(), "%s is not found.", g_p.second); + auto shape = iter->second->Var()->GetShape(); + size_t size = framework::SizeOfType(iter->second->Var()->GetDataType()); + std::for_each(shape.begin(), shape.end(), + [&size](const int64_t &n) { size *= n; }); + gps_size += size; + out << string::Sprintf("(%s(%d), %s)", g_p.second, size, g_p.first); } - VLOG(10) << out.str(); + VLOG(10) << out.str() + << ", group size:" << group_grads_params->at(i).size() + << ", group memory size:" << static_cast(gps_size) / kMB + << "(MB)"; } } void SetGroupAccordingToMemorySize( const std::unordered_map &var_nodes, details::GroupGradsAndParams *group_grads_params) const { - const uint64_t group_memory_size = GetFuseParameterMemorySize(); - if (group_memory_size == 0) { + const double group_memory_size = GetFuseParameterMemorySize(); + if (group_memory_size <= 0.0) { return; } details::GroupGradsAndParams local_group_grads_params; @@ -248,7 +267,14 @@ class AllocContinuousSpaceForGradPass : public ir::Pass { group_p_g.insert(group_p_g.end(), group_grads_params->at(j).begin(), group_grads_params->at(j).end()); ++j; - if (local_group_memory_size >= group_memory_size) { + if (GetFuseParameterGroupsSize() > 1 && + group_p_g.size() > + static_cast(GetFuseParameterGroupsSize())) { + break; + } + + if (static_cast(local_group_memory_size) / kMB >= + group_memory_size) { break; } } @@ -257,60 +283,10 @@ class AllocContinuousSpaceForGradPass : public ir::Pass { std::swap(*group_grads_params, local_group_grads_params); VLOG(10) << string::Sprintf( - "SetGroupAccordingToMemorySize(memory_size: %d):", group_memory_size); - for (size_t i = 0; i < group_grads_params->size(); ++i) { - VLOG(10) << "group " << i; - std::stringstream out; - for (auto &g_p : group_grads_params->at(i)) { - auto iter = var_nodes.find(g_p.second); - PADDLE_ENFORCE(iter != var_nodes.end(), "%s is not found.", g_p.second); - auto shape = iter->second->Var()->GetShape(); - size_t size = framework::SizeOfType(iter->second->Var()->GetDataType()); - std::for_each(shape.begin(), shape.end(), - [&size](const int64_t &n) { size *= n; }); - out << string::Sprintf("(%s(%d), %s)", g_p.second, size, g_p.first); - } - VLOG(10) << out.str(); - } - } - - void SetGroupAccordingToGroupSize( - const std::unordered_map &var_nodes, - details::GroupGradsAndParams *group_grads_params) const { - if (GetFuseParameterGroupsSize() == 1) { - return; - } - const int group_size = GetFuseParameterGroupsSize() == -1 - ? static_cast(group_grads_params->size()) - : GetFuseParameterGroupsSize(); - PADDLE_ENFORCE_GT(group_size, 1); - size_t groups = (group_grads_params->size() + group_size - 1) / group_size; - details::GroupGradsAndParams local_group_grads_params; - local_group_grads_params.reserve(groups); - - size_t j = 0; - for (size_t i = 0; i < groups; ++i) { - local_group_grads_params.emplace_back(); - auto &group_p_g = local_group_grads_params.back(); - group_p_g.reserve(group_size); - while (j < group_grads_params->size()) { - group_p_g.insert(group_p_g.end(), group_grads_params->at(j).begin(), - group_grads_params->at(j).end()); - ++j; - if (j % group_size == 0) break; - } - } - std::swap(*group_grads_params, local_group_grads_params); + "SetGroupAccordingToMemorySize(memory_size: %f):", group_memory_size); - VLOG(10) << string::Sprintf("SetGroupAccordingToGroupSize(group_size: %d):", - group_size); - for (size_t i = 0; i < group_grads_params->size(); ++i) { - VLOG(10) << "group " << i; - std::stringstream out; - for (auto &p_g : group_grads_params->at(i)) { - out << "(" << p_g.second << ", " << p_g.first << "), "; - } - VLOG(10) << out.str(); + if (VLOG_IS_ON(10)) { + PrintGroupInfo(var_nodes, group_grads_params); } } diff --git a/paddle/fluid/framework/ir/alloc_continuous_space_for_grad_pass.h b/paddle/fluid/framework/ir/alloc_continuous_space_for_grad_pass.h index b20eda96f0fb622ccd318d9418ddb15f2997f8e6..38dc4c99fc27f03d64704b479478065b636af63a 100644 --- a/paddle/fluid/framework/ir/alloc_continuous_space_for_grad_pass.h +++ b/paddle/fluid/framework/ir/alloc_continuous_space_for_grad_pass.h @@ -21,8 +21,8 @@ namespace ir { void SetFuseParameterGroupsSize(int group_size); int GetFuseParameterGroupsSize(); -void SetFuseParameterMemorySize(uint64_t memory_size); -uint64_t GetFuseParameterMemorySize(); +void SetFuseParameterMemorySize(double memory_size); +double GetFuseParameterMemorySize(); } // namespace ir } // namespace framework diff --git a/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc b/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc index 5a82d7927f4cf3ca7e7b27ecdb71eab69e007efb..c4ffb2a9de4970abd147ce2fd709977e26eb626b 100644 --- a/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc +++ b/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc @@ -136,22 +136,22 @@ void PrepareLSTMBias(const LoDTensor& B_forget, const LoDTensor& B_input, void PrepareParameters(Graph* graph, const Param& param) { // Check parameters PADDLE_ENFORCE(graph->Has(kParamScopeAttr)); - auto* scope = graph->Get(kParamScopeAttr); + auto& scope = graph->Get(kParamScopeAttr); // Create new parameters. - scope->Var(param.LSTMWeight)->GetMutable(); - scope->Var(param.LSTMBias)->GetMutable(); - scope->Var(param.Hidden)->GetMutable(); - scope->Var(param.Cell)->GetMutable(); - scope->Var(param.AttentionedX)->GetMutable(); - scope->Var(param.AttentionFCOut)->GetMutable(); - scope->Var(param.LSTMX)->GetMutable(); - scope->Var(param.LSTMOUT)->GetMutable(); + scope.Var(param.LSTMWeight)->GetMutable(); + scope.Var(param.LSTMBias)->GetMutable(); + scope.Var(param.Hidden)->GetMutable(); + scope.Var(param.Cell)->GetMutable(); + scope.Var(param.AttentionedX)->GetMutable(); + scope.Var(param.AttentionFCOut)->GetMutable(); + scope.Var(param.LSTMX)->GetMutable(); + scope.Var(param.LSTMOUT)->GetMutable(); #define GATE_W(name__) \ - auto* W_##name__##_w0 = scope->FindVar(#name__ ".w_0"); \ - auto* W_##name__##_w1 = scope->FindVar(#name__ ".w_1"); \ - auto* W_##name__##_b0 = scope->FindVar(#name__ ".b_0"); \ + auto* W_##name__##_w0 = scope.FindVar(#name__ ".w_0"); \ + auto* W_##name__##_w1 = scope.FindVar(#name__ ".w_1"); \ + auto* W_##name__##_b0 = scope.FindVar(#name__ ".b_0"); \ CHECK_P3(W_##name__##_w0, W_##name__##_w1, W_##name__##_b0); \ VLOG(4) << #name__ "_w0" \ << " shape: " << W_##name__##_w0->Get().dims(); \ @@ -169,26 +169,26 @@ void PrepareParameters(Graph* graph, const Param& param) { GATE_W(c); #undef GATE_W - auto* attention_fc_w = scope->FindVar("attention_fc.w_0"); - auto* attention_fc_b = scope->FindVar("attention_fc.b_0"); - auto* attention_output_w = scope->FindVar("attention_output.w_0"); - auto* attention_output_b = scope->FindVar("attention_output.b_0"); + auto* attention_fc_w = scope.FindVar("attention_fc.w_0"); + auto* attention_fc_b = scope.FindVar("attention_fc.b_0"); + auto* attention_output_w = scope.FindVar("attention_output.w_0"); + auto* attention_output_b = scope.FindVar("attention_output.b_0"); CHECK_P4(attention_fc_w, attention_fc_b, attention_output_w, attention_output_b); - auto* lstm_weight = scope->Var(param.LSTMWeight); + auto* lstm_weight = scope.Var(param.LSTMWeight); auto* lstm_weight_t = lstm_weight->GetMutable(); - auto* lstm_bias = scope->Var(param.LSTMBias); + auto* lstm_bias = scope.Var(param.LSTMBias); auto* lstm_bias_t = lstm_bias->GetMutable(); // reshape attention_bias auto* attention_bias_t = - scope->FindVar(param.AttentionBias)->GetMutable(); + scope.FindVar(param.AttentionBias)->GetMutable(); PADDLE_ENFORCE_EQ(attention_bias_t->dims().size(), 1); attention_bias_t->Resize(make_ddim({1, attention_bias_t->dims()[0]})); auto* attention_scalar_bias_t = - scope->FindVar(param.AttentionScalarBias)->GetMutable(); + scope.FindVar(param.AttentionScalarBias)->GetMutable(); attention_scalar_bias_t->Resize( make_ddim({1, attention_scalar_bias_t->dims()[0]})); diff --git a/paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.cc b/paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.cc index 3a6bbe65b369341c2a142dfcb261f5646d782796..6462e7bf4c099a1abb98a77d905067628b8eb88c 100644 --- a/paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.cc +++ b/paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.cc @@ -151,11 +151,11 @@ static int BuildFusion(Graph* graph, const std::string& name_scope, op_desc.SetAttr("use_seq", true); PADDLE_ENFORCE(graph->Has(kParamScopeAttr)); - auto* scope = graph->Get(kParamScopeAttr); + auto& scope = graph->Get(kParamScopeAttr); #define OP_SET_OUT(x) \ const std::string x = patterns::UniqueKey(#x); \ op_desc.SetOutput(#x, {x}); \ - scope->Var(x)->GetMutable() + scope.Var(x)->GetMutable() OP_SET_OUT(BatchedCell); OP_SET_OUT(BatchedHidden); OP_SET_OUT(ReorderedH0); diff --git a/paddle/fluid/framework/ir/fc_fuse_pass.cc b/paddle/fluid/framework/ir/fc_fuse_pass.cc index cd8030519ccfcfab3741424e8a60e9c29b698593..102fd388658447e5de86a1977934489ae2dfec27 100644 --- a/paddle/fluid/framework/ir/fc_fuse_pass.cc +++ b/paddle/fluid/framework/ir/fc_fuse_pass.cc @@ -13,6 +13,7 @@ // limitations under the License. #include "paddle/fluid/framework/ir/fc_fuse_pass.h" +#include #include #include #include @@ -77,9 +78,15 @@ void FCFusePass::ApplyImpl(ir::Graph* graph) const { desc.SetAttr("enable_int8", base_op_desc->GetAttr("enable_int8")); desc.SetAttr("input_scale", base_op_desc->GetAttr("input_scale")); desc.SetAttr("weight_scale", base_op_desc->GetAttr("weight_scale")); + if (base_op_desc->HasAttr("out_scale")) + desc.SetAttr("out_scale", base_op_desc->GetAttr("out_scale")); + auto elementwise_desc = elementwise_add->Op(); + if (elementwise_desc->HasAttr("out_scale")) + desc.SetAttr("out_scale", elementwise_desc->GetAttr("out_scale")); } desc.SetType("fc"); + auto fc_node = g->CreateOpNode(&desc); // OpDesc will be copied. GraphSafeRemoveNodes(graph, {mul, elementwise_add, mul_out}); diff --git a/paddle/fluid/framework/ir/fc_gru_fuse_pass.cc b/paddle/fluid/framework/ir/fc_gru_fuse_pass.cc index 5f660c6d366fe094aed84ed2aa2f05adcbebbc43..10cbe319ac88dff5b84817dd6e1758f0332b8115 100644 --- a/paddle/fluid/framework/ir/fc_gru_fuse_pass.cc +++ b/paddle/fluid/framework/ir/fc_gru_fuse_pass.cc @@ -69,16 +69,15 @@ static int BuildFusion(Graph* graph, const std::string& name_scope, auto* op = graph->CreateOpNode(&op_desc); PADDLE_ENFORCE(graph->Has(kParamScopeAttr)); - auto* scope = graph->Get(kParamScopeAttr); - PADDLE_ENFORCE(scope); + auto& scope = graph->Get(kParamScopeAttr); if (with_fc_bias) { // Fusion GRU bias = fcbias + grubias - auto* fusion_bias_var = scope->Var(NEW_NAME(bias) + bias->Name()); + auto* fusion_bias_var = scope.Var(NEW_NAME(bias) + bias->Name()); auto* out_bias_tensor = fusion_bias_var->GetMutable(); PADDLE_ENFORCE(fusion_bias_var); - auto* gru_bias_var = scope->FindVar(bias->Name()); - auto* fc_bias_var = scope->FindVar(fc_bias->Name()); + auto* gru_bias_var = scope.FindVar(bias->Name()); + auto* fc_bias_var = scope.FindVar(fc_bias->Name()); PADDLE_ENFORCE(gru_bias_var); PADDLE_ENFORCE(fc_bias_var); const auto& gru_bias_tenosr = gru_bias_var->Get(); @@ -94,7 +93,7 @@ static int BuildFusion(Graph* graph, const std::string& name_scope, #undef GET_NODE #define NEW_IMTERMEDIATE_OUT(key) \ - scope->Var(NEW_NAME(key))->GetMutable() + scope.Var(NEW_NAME(key))->GetMutable() NEW_IMTERMEDIATE_OUT(ReorderedH0); NEW_IMTERMEDIATE_OUT(XX); NEW_IMTERMEDIATE_OUT(BatchedInput); diff --git a/paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc b/paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc index babeba96149247fda20a1621a580cdcdbc2750d1..6858a98be397b42874f7b0e4f487caa754b9c1bd 100644 --- a/paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc +++ b/paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc @@ -100,11 +100,11 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope, op_desc.SetAttr("use_seq", true); PADDLE_ENFORCE(graph->Has(kParamScopeAttr)); - auto* scope = graph->Get(kParamScopeAttr); + auto& scope = graph->Get(kParamScopeAttr); #define OP_SET_OUT(x) \ const std::string x = patterns::UniqueKey(#x); \ op_desc.SetOutput(#x, {x}); \ - scope->Var(x)->GetMutable() + scope.Var(x)->GetMutable() OP_SET_OUT(BatchedCell); OP_SET_OUT(BatchedHidden); OP_SET_OUT(ReorderedH0); diff --git a/paddle/fluid/framework/ir/fuse_elewise_add_act_pass.cc b/paddle/fluid/framework/ir/fuse_elewise_add_act_pass.cc index bd49673168377486cd81726ce623e7196270d6a0..7f9eccf2fdd4ee7955b90fe20b91250e5b498f32 100644 --- a/paddle/fluid/framework/ir/fuse_elewise_add_act_pass.cc +++ b/paddle/fluid/framework/ir/fuse_elewise_add_act_pass.cc @@ -26,7 +26,7 @@ namespace framework { namespace ir { void FuseElewiseAddActPass::ApplyImpl(ir::Graph *graph) const { - std::unordered_set act_types = {"relu", "scale"}; + std::unordered_set act_types = {"relu", "scale", "tanh"}; graph = FuseActElewiseAdd(graph, act_types); graph = FuseElewiseAddAct(graph, act_types); // backward diff --git a/paddle/fluid/framework/ir/fuse_pass_base.cc b/paddle/fluid/framework/ir/fuse_pass_base.cc index d70010089e4b4fbb4542ef7748b8e9ece48d3942..5e2523607d6973b53e7fd68394ae887c5e14b09d 100644 --- a/paddle/fluid/framework/ir/fuse_pass_base.cc +++ b/paddle/fluid/framework/ir/fuse_pass_base.cc @@ -13,6 +13,7 @@ // limitations under the License. #include "paddle/fluid/framework/ir/fuse_pass_base.h" +#include namespace paddle { namespace framework { @@ -25,7 +26,8 @@ void FusePassBase::Init(const std::string& repr, Graph* graph) const { Scope* FusePassBase::param_scope() const { PADDLE_ENFORCE(graph_->Has(kParamScopeAttr)); - return graph_->Get(kParamScopeAttr); + auto& scope = graph_->Get(kParamScopeAttr); + return &scope; } void FusePassBase::AddStatis(int count_of_fused) const { @@ -55,7 +57,7 @@ FuseOptions FusePassBase::FindFuseOption(const Node& node1, #else return FUSE_NATIVE; #endif -}; +} } // namespace ir } // namespace framework diff --git a/paddle/fluid/framework/ir/graph.cc b/paddle/fluid/framework/ir/graph.cc index 5eba32c4f3a846183d9bbad51b77a29cfca677f0..8ba0e8b80b1c69cad8f8796974828575da343ce8 100644 --- a/paddle/fluid/framework/ir/graph.cc +++ b/paddle/fluid/framework/ir/graph.cc @@ -134,6 +134,7 @@ void Graph::ResolveHazard( ir::Node *dep_var = CreateControlDepVar(); write_op->inputs.push_back(dep_var); upstream_op->outputs.push_back(dep_var); + VLOG(10) << "add dep_var:" << dep_var->Name(); dep_var->outputs.push_back(write_op); dep_var->inputs.push_back(upstream_op); } @@ -157,6 +158,7 @@ void Graph::ResolveHazard( if (has_dep) continue; ir::Node *dep_var = CreateControlDepVar(); + VLOG(10) << "add dep_var:" << dep_var->Name(); read_op->outputs.push_back(dep_var); dep_var->inputs.push_back(read_op); write_op->inputs.push_back(dep_var); diff --git a/paddle/fluid/framework/ir/graph_pattern_detector.cc b/paddle/fluid/framework/ir/graph_pattern_detector.cc index 0dcf064902d1c1c6cb034421cedea0387b6e0505..15b3429ef170a7e750b2a4d004ba21100a8071ef 100644 --- a/paddle/fluid/framework/ir/graph_pattern_detector.cc +++ b/paddle/fluid/framework/ir/graph_pattern_detector.cc @@ -14,7 +14,10 @@ #include #include +#include #include +#include +#include #include #include "paddle/fluid/framework/ir/graph_helper.h" @@ -785,6 +788,33 @@ PDNode *patterns::ConvReLU::operator()( return relu_out_var; } +PDNode *patterns::ConvBReLU::operator()( + paddle::framework::ir::PDNode *conv_input) { + // Create Operators + conv_input->assert_is_op_input("conv2d", "Input"); + auto *conv_op = pattern->NewNode(conv_repr())->assert_is_op("conv2d"); + auto *brelu_op = pattern->NewNode(brelu_repr())->assert_is_op("relu6"); + // Create variables + // Filter + auto *conv_weight_var = pattern->NewNode(conv_weight_repr()) + ->AsInput() + ->assert_is_persistable_var() + ->assert_is_op_input("conv2d", "Filter"); + // intermediate variable, will be removed in the IR after fuse. + auto *conv_out_var = pattern->NewNode(conv_out_repr()) + ->AsIntermediate() + ->assert_is_only_output_of_op("conv2d") + ->assert_is_op_input("relu6"); + // output + auto *brelu_out_var = pattern->NewNode(brelu_out_repr()) + ->AsOutput() + ->assert_is_op_output("relu6"); + + conv_op->LinksFrom({conv_input, conv_weight_var}).LinksTo({conv_out_var}); + brelu_op->LinksFrom({conv_out_var}).LinksTo({brelu_out_var}); + return brelu_out_var; +} + PDNode *patterns::SeqConvEltAddRelu::operator()( paddle::framework::ir::PDNode *seqconv_input) { // Create Operators @@ -869,6 +899,33 @@ PDNode *patterns::FC::operator()(paddle::framework::ir::PDNode *x, } } +PDNode *patterns::FCMKLDNN::operator()(paddle::framework::ir::PDNode *x, + bool with_bias) { + // Create shared nodes. + x->assert_is_op_input("fc", "Input"); + + auto *fc_op = pattern->NewNode(fc_repr())->assert_is_op("fc"); + // Create variables + // Filter + auto *fc_weight_var = pattern->NewNode(weights_repr()) + ->AsInput() + ->assert_is_persistable_var() + ->assert_is_op_input("fc", "W"); + // Bias + auto *fc_bias_var = pattern->NewNode(bias_repr()) + ->AsInput() + ->assert_is_persistable_var() + ->assert_is_op_input("fc", "Bias"); + // Output + auto *fc_out_var = pattern->NewNode(output_repr()) + ->AsOutput() + ->assert_is_op_output("fc", "Out") + ->assert_is_only_output_of_op("fc"); + + fc_op->LinksFrom({x, fc_weight_var, fc_bias_var}).LinksTo({fc_out_var}); + return fc_out_var; +} + PDNode *patterns::Embedding::operator()(PDNode *x) { x->assert_is_op_input("lookup_table", "Ids"); auto *lookup_table_op = @@ -1035,12 +1092,12 @@ PDNode *patterns::ElewiseAddActInplaceGrad::operator()( return ele_add_grad; } +// conv_type: conv2d, conv3d, conv2d_transpose PDNode *patterns::ConvBias::operator()( - paddle::framework::ir::PDNode *conv_input, bool is_conv3d) { - std::string type = is_conv3d ? "conv3d" : "conv2d"; + paddle::framework::ir::PDNode *conv_input, std::string conv_type) { // Create Operators - conv_input->assert_is_op_input(type, "Input"); - auto *conv_op = pattern->NewNode(conv_repr())->assert_is_op(type); + conv_input->assert_is_op_input(conv_type, "Input"); + auto *conv_op = pattern->NewNode(conv_repr())->assert_is_op(conv_type); auto *eltiwse_op = pattern->NewNode(eltwise_repr())->assert_is_op("elementwise_add"); // Create variables @@ -1048,11 +1105,11 @@ PDNode *patterns::ConvBias::operator()( auto *conv_weight_var = pattern->NewNode(conv_weight_repr()) ->AsInput() ->assert_is_persistable_var() - ->assert_is_op_input(type, "Filter"); + ->assert_is_op_input(conv_type, "Filter"); // intermediate variable, will be removed in the IR after fuse. auto *conv_out_var = pattern->NewNode(conv_out_repr()) ->AsIntermediate() - ->assert_is_only_output_of_op(type) + ->assert_is_only_output_of_op(conv_type) ->assert_is_op_input("elementwise_add"); // Bias stored in elementwise_add auto *eltwise_bias_var = pattern->NewNode(eltwise_bias_repr()) @@ -1157,6 +1214,57 @@ PDNode *patterns::ElementwiseAdd::operator()(PDNode *x_var, PDNode *y_var) { return out_var; } +PDNode *patterns::Concat::operator()() { + auto concat_op = pattern->NewNode(concat_op_repr())->assert_is_op("concat"); + + auto output_var = pattern->NewNode(concat_out_repr()) + ->AsOutput() + ->assert_is_op_output("concat", "Out"); + + concat_op->LinksTo({output_var}); + return output_var; +} + +PDNode *patterns::ConcatReLU::operator()() { + auto concat_op = pattern->NewNode(concat_op_repr())->assert_is_op("concat"); + auto relu_op = pattern->NewNode(relu_op_repr())->assert_is_op("relu"); + + auto concat_out = + pattern->NewNode(concat_out_repr())->assert_is_op_output("concat", "Out"); + + auto relu_out = pattern->NewNode(relu_out_repr()) + ->AsOutput() + ->assert_is_op_output("relu", "Out"); + + concat_op->LinksTo({concat_out}); + relu_op->LinksFrom({concat_out}).LinksTo({relu_out}); + + return relu_out; +} + +PDNode *patterns::ConvConcatReLU::operator()() { + auto conv_op = pattern->NewNode(conv_op_repr())->assert_is_op("conv2d"); + auto concat_op = pattern->NewNode(concat_op_repr())->assert_is_op("concat"); + auto relu_op = pattern->NewNode(relu_op_repr())->assert_is_op("relu"); + + auto conv_out = pattern->NewNode(conv_out_repr()) + ->assert_is_op_output("conv2d", "Output"); + + auto concat_out = pattern->NewNode(concat_out_repr()) + ->assert_is_op_output("concat", "Out") + ->assert_is_op_input("relu", "X"); + + auto relu_out = pattern->NewNode(relu_out_repr()) + ->AsOutput() + ->assert_is_op_output("relu", "Out"); + + conv_op->LinksTo({conv_out}); + concat_op->LinksFrom({conv_out}).LinksTo({concat_out}); + relu_op->LinksFrom({concat_out}).LinksTo({relu_out}); + + return relu_out; +} + std::unordered_set conv_act_set({"identity", "relu"}); PDNode *patterns::ConvElementwiseaddAct::operator()(PDNode *conv_in) { @@ -1641,13 +1749,16 @@ void patterns::QuantDequantOpFuse::operator()(PDNode *quant_op_input, const std::string &op_type, const std::string &weight_name, int times, - const std::string &quant_type) { - const int kNumFields = 5; + const std::string &quant_type, + const std::string &dequant_type) { + int kNumFields = 5; const int kQuantizedWeightOffset = 0; const int kQuantizedOpOffset = 1; const int kQuantizedOpOutOffset = 2; const int kDequantOpOffset = 3; const int kDequantOpOutOffset = 4; + const int kDequantOpWeightScaleOffset = 5; + // the quant op always be one. auto quant_op_in_scale = pattern->NewNode(GetNodeName("quant_op_in_scale")) ->assert_is_op_input(quant_type, "InScale") @@ -1655,11 +1766,19 @@ void patterns::QuantDequantOpFuse::operator()(PDNode *quant_op_input, auto quant_op = pattern->NewNode(GetNodeName("quant_op"))->assert_is_op(quant_type); - auto quant_op_out_scale = - pattern->NewNode(GetNodeName("quant_op_out_scale")) - ->assert_is_op_output(quant_type, "OutScale") - ->assert_is_op_input("fake_dequantize_max_abs", "Scale") - ->AsIntermediate(); + PDNode *quant_op_out_scale = nullptr; + if (dequant_type == "fake_channel_wise_dequantize_max_abs") { + kNumFields += 1; + quant_op_out_scale = pattern->NewNode(GetNodeName("quant_op_out_scale")) + ->assert_is_op_output(quant_type, "OutScale") + ->assert_is_op_nth_input(dequant_type, "Scales", 1) + ->AsIntermediate(); + } else { + quant_op_out_scale = pattern->NewNode(GetNodeName("quant_op_out_scale")) + ->assert_is_op_output(quant_type, "OutScale") + ->assert_is_op_input(dequant_type, "Scale") + ->AsIntermediate(); + } auto quant_op_out = pattern->NewNode(GetNodeName("quant_op_out")) ->assert_is_op_output(quant_type, "Out") @@ -1680,16 +1799,25 @@ void patterns::QuantDequantOpFuse::operator()(PDNode *quant_op_input, nodes.push_back( pattern->NewNode(GetNodeName("quantized_op_out") + std::to_string(i)) ->assert_is_op_output(op_type) - ->assert_is_op_input("fake_dequantize_max_abs", "X") + ->assert_is_op_input(dequant_type, "X") ->AsIntermediate()); nodes.push_back( pattern->NewNode(GetNodeName("dequant_op") + std::to_string(i)) - ->assert_is_op("fake_dequantize_max_abs")); + ->assert_is_op(dequant_type)); + nodes.push_back( pattern->NewNode(GetNodeName("dequant_op_out") + std::to_string(i)) - ->assert_is_op_output("fake_dequantize_max_abs", "Out") + ->assert_is_op_output(dequant_type, "Out") ->AsOutput()); + + if (dequant_type == "fake_channel_wise_dequantize_max_abs") { + nodes.push_back(pattern + ->NewNode(GetNodeName("dequant_channel_scale") + + std::to_string(i)) + ->assert_is_op_nth_input(dequant_type, "Scales", 0) + ->AsInput()); + } } quant_op->LinksFrom({quant_op_input, quant_op_in_scale}); @@ -1699,8 +1827,14 @@ void patterns::QuantDequantOpFuse::operator()(PDNode *quant_op_input, {quant_op_out, nodes[i * kNumFields + kQuantizedWeightOffset]}); nodes[i * kNumFields + kQuantizedOpOutOffset]->LinksFrom( {nodes[i * kNumFields + kQuantizedOpOffset]}); - nodes[i * kNumFields + kDequantOpOffset]->LinksFrom( - {nodes[i * kNumFields + kQuantizedOpOutOffset], quant_op_out_scale}); + if (dequant_type == "fake_channel_wise_dequantize_max_abs") { + nodes[i * kNumFields + kDequantOpOffset]->LinksFrom( + {nodes[i * kNumFields + kQuantizedOpOutOffset], quant_op_out_scale, + nodes[i * kNumFields + kDequantOpWeightScaleOffset]}); + } else { + nodes[i * kNumFields + kDequantOpOffset]->LinksFrom( + {nodes[i * kNumFields + kQuantizedOpOutOffset], quant_op_out_scale}); + } nodes[i * kNumFields + kDequantOpOutOffset]->LinksFrom( {nodes[i * kNumFields + kDequantOpOffset]}); } @@ -1737,6 +1871,41 @@ void patterns::ShuffleChannelPattern::operator()(PDNode *reshape1_in) { reshape2_out->LinksFrom({reshape2_op}); } +void patterns::DeleteQuantDequantOpPattern::operator()() { + auto any_op_out = + pattern->NewNode(any_op_out_repr()) + ->assert_is_op_input( + "fake_quantize_dequantize_moving_average_abs_max", "X") + ->AsInput(); + + auto quant_dequant_op_inscale = + pattern->NewNode(quant_dequant_op_inscale_repr()) + ->assert_is_op_input( + "fake_quantize_dequantize_moving_average_abs_max", "InScale") + ->AsInput(); + auto quant_dequant_op = + pattern->NewNode(quant_dequant_op_repr()) + ->assert_is_op("fake_quantize_dequantize_moving_average_abs_max"); + + auto quant_dequant_out = + pattern->NewNode(quant_dequant_op_out_repr()) + ->assert_is_op_output( + "fake_quantize_dequantize_moving_average_abs_max", "Out") + ->AsIntermediate(); + + auto quant_dequant_op_outscale = + pattern->NewNode(quant_dequant_op_outscale_repr()) + ->assert_is_op_output( + "fake_quantize_dequantize_moving_average_abs_max", "OutScale") + ->AsOutput(); + auto any_op2 = pattern->NewNode(any_op2_repr())->assert_is_op()->AsOutput(); + + quant_dequant_op->LinksFrom({any_op_out, quant_dequant_op_inscale}); + quant_dequant_op_outscale->LinksFrom({quant_dequant_op}); + quant_dequant_out->LinksFrom({quant_dequant_op}); + any_op2->LinksFrom({quant_dequant_out}); +} + } // namespace ir } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/ir/graph_pattern_detector.h b/paddle/fluid/framework/ir/graph_pattern_detector.h index 907371b56b06dcd66297adedea6c17b61d9b5e38..1c53b9105225e6840bacb2edbe6ffe373ac16110 100644 --- a/paddle/fluid/framework/ir/graph_pattern_detector.h +++ b/paddle/fluid/framework/ir/graph_pattern_detector.h @@ -449,6 +449,27 @@ struct ConvReLU : public PatternBase { PATTERN_DECL_NODE(relu_out); }; +// CONV with ReLU6 +// op: conv + relu6 +// named nodes: +// conv_input, conv_weight, +// conv_out, conv, +// relu6_out, relu6 +struct ConvBReLU : public PatternBase { + ConvBReLU(PDPattern* pattern, const std::string& name_scope) + : PatternBase(pattern, name_scope, "conv_bounded_relu") {} + + PDNode* operator()(PDNode* conv_input); + + // declare operator node's name + PATTERN_DECL_NODE(conv); + PATTERN_DECL_NODE(brelu); + // declare variable node's name + PATTERN_DECL_NODE(conv_weight); + PATTERN_DECL_NODE(conv_out); + PATTERN_DECL_NODE(brelu_out); +}; + // SEQCONV with Elementwise_Add ReLU // op: seqconv + elementwise_add + relu // named nodes: @@ -496,6 +517,25 @@ struct FC : public PatternBase { PATTERN_DECL_NODE(Out); }; +// MKL-DNN's FC with bias +// op: fc +// named node: +// fc +// w, bias, output +struct FCMKLDNN : public PatternBase { + FCMKLDNN(PDPattern* pattern, const std::string& name_scope) + : PatternBase(pattern, name_scope, "fc_mkldnn") {} + + PDNode* operator()(PDNode* x, bool with_bias); + + // declare operator node's name + PATTERN_DECL_NODE(fc); + // declare variable node's name + PATTERN_DECL_NODE(weights); + PATTERN_DECL_NODE(bias); + PATTERN_DECL_NODE(output); +}; + // Embedding struct Embedding : public PatternBase { Embedding(PDPattern* pattern, const std::string& name_scope) @@ -629,7 +669,7 @@ struct ElewiseAddActInplaceGrad : public PatternBase { struct ConvBias : public PatternBase { ConvBias(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_bias") {} - PDNode* operator()(PDNode* conv_input, bool is_conv3d = false); + PDNode* operator()(PDNode* conv_input, std::string conv_type = "conv2d"); // declare operator node's name PATTERN_DECL_NODE(conv); PATTERN_DECL_NODE(eltwise); @@ -707,6 +747,52 @@ struct ElementwiseAdd : public PatternBase { PATTERN_DECL_NODE(elementwise_add_out); }; +// Concat op +// Forward pass for concat. +// concat_out is a result of the operator. +struct Concat : public PatternBase { + Concat(PDPattern* pattern, const std::string& name_scope) + : PatternBase(pattern, name_scope, "concat") {} + + PDNode* operator()(); + + PATTERN_DECL_NODE(concat_op); + PATTERN_DECL_NODE(concat_out); +}; + +// Concat + ReLU +// named nodes: +// concat_op, concat_out, relu_op, relu_out +struct ConcatReLU : public PatternBase { + ConcatReLU(PDPattern* pattern, const std::string& name_scope) + : PatternBase(pattern, name_scope, "concat_relu") {} + + PDNode* operator()(); + + PATTERN_DECL_NODE(concat_op); + PATTERN_DECL_NODE(concat_out); + PATTERN_DECL_NODE(relu_op); + PATTERN_DECL_NODE(relu_out); +}; + +// Conv + Concat + ReLU +// named nodes: +// conv_op, conv_out +// concat_op, concat_out, relu_op, relu_out +struct ConvConcatReLU : public PatternBase { + ConvConcatReLU(PDPattern* pattern, const std::string& name_scope) + : PatternBase(pattern, name_scope, "conv_concat_relu") {} + + PDNode* operator()(); + + PATTERN_DECL_NODE(conv_op); + PATTERN_DECL_NODE(conv_out); + PATTERN_DECL_NODE(concat_op); + PATTERN_DECL_NODE(concat_out); + PATTERN_DECL_NODE(relu_op); + PATTERN_DECL_NODE(relu_out); +}; + // Conv + ElementwiseAdd + an activation // This pattern can futher fuse the conv related ops after the conv+bn fusion. struct ConvElementwiseaddAct : public PatternBase { @@ -881,7 +967,8 @@ struct QuantDequantOpFuse : public PatternBase { void operator()(PDNode* quant_op_input, const std::string& op_name, const std::string& weight_name, int times, - const std::string& quant_type); + const std::string& quant_type, + const std::string& dequant_type); std::string GetNodeName(const std::string& op_type) { return PDNodeName(name_scope_, repr_, id_, op_type); @@ -907,6 +994,20 @@ struct ShuffleChannelPattern : public PatternBase { PATTERN_DECL_NODE(reshape2_out); }; +struct DeleteQuantDequantOpPattern : public PatternBase { + DeleteQuantDequantOpPattern(PDPattern* pattern, const std::string& name_scope) + : PatternBase(pattern, name_scope, "delete_quantdequant_op_pattern") {} + + void operator()(); + + PATTERN_DECL_NODE(any_op_out); + PATTERN_DECL_NODE(quant_dequant_op_inscale); + PATTERN_DECL_NODE(quant_dequant_op); + PATTERN_DECL_NODE(quant_dequant_op_outscale); + PATTERN_DECL_NODE(quant_dequant_op_out); + PATTERN_DECL_NODE(any_op2); +}; + } // namespace patterns // Link two ir::Nodes from each other. diff --git a/paddle/fluid/framework/ir/lock_free_optimize_pass.h b/paddle/fluid/framework/ir/lock_free_optimize_pass.h index d1718857a5d84304c3c02e74c7ca79c24f367f8c..9c923480bac26fb8c68768c8365b0f899959ec64 100644 --- a/paddle/fluid/framework/ir/lock_free_optimize_pass.h +++ b/paddle/fluid/framework/ir/lock_free_optimize_pass.h @@ -12,8 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_ -#define PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_ +#pragma once #include #include @@ -126,5 +125,3 @@ class LockFreeOptimizePass : public Pass { } // namespace ir } // namespace framework } // namespace paddle - -#endif // PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_ diff --git a/paddle/fluid/framework/ir/memory_optimize_pass/inplace_op_pass.cc b/paddle/fluid/framework/ir/memory_optimize_pass/inplace_op_pass.cc index ed746ea988e82dff23257996f688e55c56f09168..f57e7bb2301b2b5115de51138f6c531fe94b2bd2 100644 --- a/paddle/fluid/framework/ir/memory_optimize_pass/inplace_op_pass.cc +++ b/paddle/fluid/framework/ir/memory_optimize_pass/inplace_op_pass.cc @@ -48,8 +48,6 @@ DEFINE_bool( "Such as scale, elementwise_add" "By default, it's turned off"); -DECLARE_string(memory_optimize_debug); - namespace paddle { namespace framework { namespace ir { @@ -461,13 +459,6 @@ void InplacePass::ApplyImpl(ir::Graph *graph) const { continue; } - // Debug Interface. Which would be skipped by the pass. - if (out_arg == FLAGS_memory_optimize_debug) { - VLOG(4) << "Skiped var by force. FLAGS_memory_optimize_debug=" - << out_node->Name(); - continue; - } - VLOG(4) << "Rename " << out_node->Name() << " with " << in_node->Name() << " in " << op_type; RenameInOut(op_node, in_node, out_node); diff --git a/paddle/fluid/framework/ir/memory_optimize_pass/memory_optimize_pass.cc b/paddle/fluid/framework/ir/memory_optimize_pass/memory_optimize_pass.cc index 8d5271b5081d0011dd653c40685d6a0bec0d5f48..af3fbb2808b0c11a5013800e41f877391a51d368 100644 --- a/paddle/fluid/framework/ir/memory_optimize_pass/memory_optimize_pass.cc +++ b/paddle/fluid/framework/ir/memory_optimize_pass/memory_optimize_pass.cc @@ -31,15 +31,6 @@ #include "paddle/fluid/framework/ir/graph.h" #include "paddle/fluid/framework/ir/graph_helper.h" -DEFINE_bool(enable_subgraph_optimize, false, - "SubGraph also reuse global graph variables, it will reduce the " - "memory occupation" - "but a higher risk of memory reuse error. default disabled."); -DEFINE_string(memory_optimize_debug, "", - "debug the operator output variable when do the variable reuse." - "memory reuse pass." - "only for debug, default disabled."); - namespace paddle { namespace framework { namespace ir { @@ -57,15 +48,6 @@ void MemoryOptimizePass::ApplyImpl(ir::Graph* graph) const { auto* op_desc = op->Op(); // some op in graph has no op desc if (op_desc == nullptr) continue; - if (OpHasSubBlock(op_desc)) { - if (FLAGS_enable_subgraph_optimize) { - SubGraphOptimize(op_desc); - } else { - VLOG(3) << op->Name() - << " has subblock, but disable subgraph optimize. skipped."; - continue; - } - } for (auto& var : op->outputs) { if (var->IsVar() && !var->IsCtrlVar() && skip_set_.count(var->Name())) { @@ -82,13 +64,6 @@ void MemoryOptimizePass::ApplyImpl(ir::Graph* graph) const { << "replace it again. Skip this candidate."; cache = pool_.FindNextBestFitNode(var, cache); } - if (var->Name() == FLAGS_memory_optimize_debug) { - VLOG(3) << "start match var " << DebugString(var) << " of op " - << op->Name(); - VLOG(3) << pool_.ToString(); - VLOG(3) << "matched in pool : " - << ((cache == nullptr) ? "False" : "True"); - } if (cache != nullptr) { int node_idx_in_pool = pool_.GetNodeIndexInPool(cache); @@ -128,81 +103,6 @@ void MemoryOptimizePass::ApplyImpl(ir::Graph* graph) const { graph->ResolveHazard(var_nodes_); } -void MemoryOptimizePass::SubGraphOptimize(OpDesc* op_desc) const { - // conditional block, while op and their grad op - auto* sub_block_desc = - AttrReader(op_desc->GetAttrMap()).Get("sub_block"); - - // create a mirror block to construct an IR Graph. - ProgramDesc prog; - auto* copy_block = prog.MutableBlock(0); - for (auto* op : sub_block_desc->AllOps()) { - auto* copy_op = copy_block->AppendOp(); - copy_op->CopyFrom(*op); - copy_op->Flush(); - } - - for (auto* var : sub_block_desc->AllVars()) { - auto* copy_var = copy_block->Var(var->Name()); - copy_var->SetDataType(var->GetDataType()); - // only lod tensor can be reused. So ignore the multiple dims case. - copy_var->SetType(var->GetType()); - copy_var->SetShape(var->GetShape()); - copy_var->SetPersistable(var->Persistable()); - } - - ir::Graph sub_graph(prog); - std::unordered_set sub_graph_all_ops; - FilterVariables(sub_graph.Nodes(), [&](ir::Node* var) { - // sub_graph_all_ops.emplace(var); - if (var->IsVar() && !var->IsCtrlVar()) { - sub_graph_all_ops.emplace(var); - } - }); - int sub_reuse_id = 0; - // subgraph nodes is unordered, reuse need to follow the desc order. - // find the right op node through the descs - for (auto* sub_op_desc : sub_block_desc->AllOps()) { - ir::Node* sub_op = nullptr; - for (auto* node : sub_graph_all_ops) { - if (node->Op() == sub_op_desc) { - sub_op = node; - break; - } - } - PADDLE_ENFORCE(sub_op != nullptr); - for (auto* var : sub_op->outputs) { - if (NodeCanReused(var)) { - ir::Node* cache = pool_.FindBestFitNode(var); - if (cache != nullptr) { - if (var->Var()->GetDataType() != cache->Var()->GetDataType()) { - continue; - } - int node_idx_in_pool = pool_.GetNodeIndexInPool(cache); - VLOG(3) << string::Sprintf( - "!!! %s, %s => %s, cache idx %d, pool size %d", - std::to_string(sub_reuse_id++), DebugString(var), - DebugString(cache), node_idx_in_pool, - static_cast(pool_.size())); - // NOTE(dzh): subblock is not in IR graph. Modify the block_desc - // immediately to make the subblock variable reuse strategy take - // effect. Because it is a single op in graph. No need to - // update the ir nodes. - // FIXME(liuwei1031): Graph is not aware of the existence of - // BlockDescs and ProgramDescs. - // The operations related to BlockDesc or ProgramDesc should perform - // on Graph or Node directly! - sub_op_desc->Rename(var->Name(), cache->Name()); - if (sub_op_desc->Block() != nullptr && - sub_op_desc->Block()->HasVar(var->Name())) { - sub_op_desc->Block()->RemoveVar(var->Name()); - } - } - } - } - } -} - void MemoryOptimizePass::CollectSkipVarsSet(ir::Graph* graph) const { // fill skip_set_ PADDLE_ENFORCE(graph->Has(kMemOptSkipVars)); diff --git a/paddle/fluid/framework/ir/memory_optimize_pass/record_skip_memory_opt_vars_pass.cc b/paddle/fluid/framework/ir/memory_optimize_pass/record_skip_memory_opt_vars_pass.cc index 075a1955eb641832dd8cc3c11befd58e798b545b..040b769f89dd6de6cf3585d1e5f83da8fdb700d3 100644 --- a/paddle/fluid/framework/ir/memory_optimize_pass/record_skip_memory_opt_vars_pass.cc +++ b/paddle/fluid/framework/ir/memory_optimize_pass/record_skip_memory_opt_vars_pass.cc @@ -140,9 +140,9 @@ class RecordSkipMemoryOptVarsPass : public ir::Pass { // fail since "states" and "ex_states" cannot be found in main block. // When memory optimization is enabled, "states", "ex_states" and their // gradient should be skipped. - auto& ex_states = + auto ex_states = boost::get>(op_desc->GetAttr("ex_states")); - auto& states = + auto states = boost::get>(op_desc->GetAttr("states")); if (op_type == "recurrent") { UpdateSkipVarSet(skip_vars, {ex_states, states}); @@ -154,7 +154,7 @@ class RecordSkipMemoryOptVarsPass : public ir::Pass { UpdateSkipVarSet( skip_vars, {ToGradVarName(op_desc->Input("parameters")), - ToGradVarName(op_desc->Input("input")), ex_states, states, + ToGradVarName(op_desc->Input("inputs")), ex_states, states, ToGradVarName(ex_states), ToGradVarName(states)}); } } diff --git a/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc b/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc index 8ef3993b065bcd37dcd571ba5a284cd35cfe052d..bbfc8c005580bb949b498e4474c4059cd09f56b3 100644 --- a/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc +++ b/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc @@ -45,16 +45,14 @@ void ConvBiasFusePass::ApplyImpl(ir::Graph* graph) const { auto* scope = param_scope(); PADDLE_ENFORCE(scope); - std::string type = is_conv3d() ? "conv3d" : "conv2d"; - GraphPatternDetector gpd; auto* conv_input = gpd.mutable_pattern() ->NewNode(patterns::PDNodeName(name_scope_, "conv_input")) ->AsInput() - ->assert_is_op_input(type, "Input"); + ->assert_is_op_input(type(), "Input"); patterns::ConvBias conv_bias_pattern(gpd.mutable_pattern(), name_scope_); - conv_bias_pattern(conv_input, is_conv3d()); + conv_bias_pattern(conv_input, type()); int found_conv_bias_count = 0; auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph, Graph* g) { @@ -75,7 +73,7 @@ void ConvBiasFusePass::ApplyImpl(ir::Graph* graph) const { // check if fuse can be done and if MKL-DNN should be used FuseOptions fuse_option = FindFuseOption(*conv, *eltwise); if (fuse_option == DO_NOT_FUSE || fuse_option == FUSE_NATIVE) { - VLOG(3) << "do not perform conv+bias fuse"; + VLOG(3) << "do not perform " + type() + "+bias fuse"; return; } @@ -110,7 +108,7 @@ void ConvBiasFusePass::ApplyImpl(ir::Graph* graph) const { desc.SetInput("Filter", std::vector({conv_weight->Name()})); desc.SetInput("Bias", std::vector({eltwise_bias->Name()})); desc.SetOutput("Output", std::vector({eltwise_out->Name()})); - desc.SetType(type); + desc.SetType(type()); for (auto& attr : conv->Op()->GetAttrMap()) { desc.SetAttr(attr.first, attr.second); @@ -135,5 +133,7 @@ void ConvBiasFusePass::ApplyImpl(ir::Graph* graph) const { } // namespace paddle REGISTER_PASS(conv_bias_mkldnn_fuse_pass, paddle::framework::ir::ConvBiasFusePass); +REGISTER_PASS(conv_transpose_bias_mkldnn_fuse_pass, + paddle::framework::ir::Conv2DTransposeBiasFusePass); REGISTER_PASS(conv3d_bias_mkldnn_fuse_pass, paddle::framework::ir::Conv3DBiasFusePass); diff --git a/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.h b/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.h index 84106d0655d5578338da3b5993f3d2ec191542fd..833fbc748ebd03377ebaa6a5fa72d334ff8b7d37 100644 --- a/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.h +++ b/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.h @@ -26,7 +26,7 @@ namespace ir { class ConvBiasFusePass : public FusePassBase { public: virtual ~ConvBiasFusePass() {} - virtual bool is_conv3d() const { return false; } + virtual std::string type() const { return "conv2d"; } protected: void ApplyImpl(ir::Graph* graph) const override; @@ -35,9 +35,14 @@ class ConvBiasFusePass : public FusePassBase { /* * Fuse the Conv3D and Elementwise_add to a Conv3DBiasOp. */ +class Conv2DTransposeBiasFusePass : public ConvBiasFusePass { + public: + std::string type() const override { return "conv2d_transpose"; } +}; + class Conv3DBiasFusePass : public ConvBiasFusePass { public: - bool is_conv3d() const override { return true; } + std::string type() const override { return "conv3d"; } }; } // namespace ir } // namespace framework diff --git a/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc b/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc index ff7f9190fdeb1648a7ff2c59a07bad399a03bf3f..427d7bc9aeb15f4adb4a486c511630836bf2bb73 100644 --- a/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc +++ b/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc @@ -81,8 +81,7 @@ void InitTensorHolder(Scope* scope, const paddle::platform::Place& place, const char* var_name) { auto x = scope->Var(var_name); auto tensor = x->GetMutable(); - tensor->mutable_data(place, proto::VarType::FP32, - ::paddle::memory::Allocator::kDefault, 1); + tensor->mutable_data(place, proto::VarType::FP32, 1); } void MainTest(bool convWithExistingBias) { @@ -97,7 +96,7 @@ void MainTest(bool convWithExistingBias) { InitTensorHolder(&scope, place, "conv_bias"); InitTensorHolder(&scope, place, "eltwise_bias"); } - graph->Set(kParamScopeAttr, new framework::Scope*(&scope)); + graph->SetNotOwned(kParamScopeAttr, &scope); auto pass = PassRegistry::Instance().Get("conv_bias_mkldnn_fuse_pass"); @@ -141,7 +140,12 @@ TEST(ConvBiasFusePass, conv_with_existing_bias) { MainTest(true); } TEST(ConvBiasFusePass, conv3d) { Conv3DBiasFusePass pass; - ASSERT_TRUE(pass.is_conv3d()); + ASSERT_EQ(pass.type(), std::string("conv3d")); +} + +TEST(ConvBiasFusePass, conv2d_transpose) { + Conv2DTransposeBiasFusePass pass; + ASSERT_EQ(pass.type(), std::string("conv2d_transpose")); } } // namespace ir diff --git a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc index dff98e523ac45ef79f3e8fd020ecd6cd7035cf92..dd3ee50e0402afe58751cc68ac0e68eb6a9dc801 100644 --- a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc +++ b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc @@ -13,6 +13,7 @@ // limitations under the License. #include "paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.h" +#include #include #include #include "paddle/fluid/framework/eigen.h" @@ -72,6 +73,53 @@ void CPUQuantizePass::QuantizeInput(Graph* g, Node* op, Node* input, if (!scale_attr_name.empty()) op->Op()->SetAttr(scale_attr_name, scale); } +void CPUQuantizePass::QuantizeInputs(Graph* g, Node* op, std::string input_name, + VarQuantScale* scales, bool are_unsigned, + std::string scale_attr_name) const { + auto inputs = op->inputs; + PADDLE_ENFORCE_GE(inputs.size(), 1); + + // create a quantize op desc prototype + OpDesc q_desc; + q_desc.SetType("quantize"); + + std::vector quantize_out_nodes(inputs.size()); + std::vector quantize_out_node_names(inputs.size()); + + double scale_min = std::numeric_limits::max(); + for (const auto& input : inputs) { + double scale = (*scales)[input->Name()].second.data()[0]; + if (scale < scale_min) scale_min = scale; + } + unsigned max = are_unsigned ? U8_MAX : S8_MAX; + float scale = scale_min * max; + + for (size_t i = 0; i < inputs.size(); i++) { + // Create quantize output variable + VarDesc quantize_out_desc(patterns::PDNodeName("quantize", "out")); + quantize_out_nodes[i] = g->CreateVarNode(&quantize_out_desc); + quantize_out_node_names[i] = quantize_out_nodes[i]->Name(); + + q_desc.SetAttr("Scale", scale); + q_desc.SetInput("Input", std::vector({inputs[i]->Name()})); + q_desc.SetOutput("Output", + std::vector({quantize_out_node_names[i]})); + q_desc.SetAttr("is_negative_input", !are_unsigned); + auto quantize_op = g->CreateOpNode(&q_desc); // OpDesc will be copied. + + // link quantize op + UnlinkNodes(inputs[i], op); + IR_NODE_LINK_TO(inputs[i], quantize_op); + IR_NODE_LINK_TO(quantize_op, quantize_out_nodes[i]); + IR_NODE_LINK_TO(quantize_out_nodes[i], op); + } + + // update op's input + op->Op()->SetInput(input_name, quantize_out_node_names); + + if (!scale_attr_name.empty()) op->Op()->SetAttr(scale_attr_name, scale); +} + void CPUQuantizePass::DequantizeOutput(Graph* g, Node* op, Node* output, std::string output_name, double scale_to_one, bool is_unsigned, @@ -216,6 +264,48 @@ void CPUQuantizePass::QuantizePool(Graph* graph) const { PrettyLogDetail("--- quantized %d pool2d ops", quantize_pool_count); } +void CPUQuantizePass::QuantizeConcat(Graph* graph) const { + GraphPatternDetector gpd; + auto pattern = gpd.mutable_pattern(); + patterns::Concat concat_pattern{pattern, name_scope_}; + concat_pattern(); + + int quantize_concat_count = 0; + auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph, + Graph* g) { + VLOG(4) << "Quantize concat op"; + GET_IR_NODE_FROM_SUBGRAPH(concat_op, concat_op, concat_pattern); + auto* concat_op_desc = concat_op->Op(); + + // skip if should not be quantized + if (!concat_op_desc->HasAttr("use_quantizer") || + !boost::get(concat_op_desc->GetAttr("use_quantizer"))) + return; + + GET_IR_NODE_FROM_SUBGRAPH(concat_out, concat_out, concat_pattern); + + // get scales calculated after warmup, they scale variables to MAX=1.0 + auto scales = Get("quant_var_scales"); + + // if all inputs were unsigned, then the output was set to unsigned + // during the scale calculation step + bool are_all_inputs_unsigned = scales[concat_out->Name()].first; + QuantizeInputs(g, concat_op, "X", &scales, are_all_inputs_unsigned); + + auto output_scale = scales[concat_out->Name()].second.data()[0]; + + DequantizeOutput(g, concat_op, concat_out, "Out", output_scale, + are_all_inputs_unsigned); + + ++quantize_concat_count; + }; + + gpd(graph, handler); + AddStatis(quantize_concat_count); + + PrettyLogDetail("--- quantized %d concat ops", quantize_concat_count); +} + void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const { VLOG(3) << "Quantizing the graph."; PADDLE_ENFORCE(graph); @@ -226,6 +316,7 @@ void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const { QuantizeConv(graph, false /* with_residual_data */); QuantizeConv(graph, true /* with_residual_data */); QuantizePool(graph); + QuantizeConcat(graph); } } // namespace ir diff --git a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.h b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.h index a178c4dc363f672fdc7c535954be0c5877a599ac..61a28fd313199b9cda46d7666f4ab53ca098eab3 100644 --- a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.h +++ b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.h @@ -48,10 +48,17 @@ class CPUQuantizePass : public FusePassBase { void QuantizePool(Graph* graph) const; + void QuantizeConcat(Graph* graph) const; + void QuantizeInput(Graph* g, Node* op, Node* input, std::string input_name, double scale_to_one, bool is_unsigned, std::string scale_attr_name = "") const; + // quantize all inputs of given name with the same (minimum) scale + void QuantizeInputs(Graph* g, Node* op, std::string input_name, + VarQuantScale* scales, bool are_unsigned, + std::string scale_attr_name = "") const; + void DequantizeOutput(Graph* g, Node* op, Node* output, std::string output_name, double scale_to_one, bool is_unsigned, diff --git a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc index 8716a412e4d5b96161c5b2e2ac06d6aa0b4e74e1..0a68944186773f84f734d81cf29dc5214d16e173 100644 --- a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc +++ b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc @@ -60,9 +60,14 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name, if (inputs.size() > 1) op->SetInput("W", {inputs[1]}); if (inputs.size() > 2) op->SetInput("Bias", {inputs[2]}); op->SetOutput("Out", {outputs[0]}); + } else if (type == "concat") { + op->SetInput("X", inputs); + op->SetOutput("Out", outputs); + op->SetAttr("use_quantizer", use_quantizer); } } +namespace { static const std::initializer_list variable_names{ "a", "w1", "c", "d", "w2", "e", "f", "g", "h", "w3", "b1", "i", "j", "w4", "b2"}; @@ -105,8 +110,7 @@ void InitTensorHolder(Scope* scope, const paddle::platform::Place& place, const char* var_name) { auto x = scope->Var(var_name); auto tensor = x->GetMutable(); - tensor->mutable_data(place, proto::VarType::FP32, - ::paddle::memory::Allocator::kDefault, 1); + tensor->mutable_data(place, proto::VarType::FP32, 1); } void MainTest(const ProgramDesc& prog, int conv_count, int pool_count, @@ -132,7 +136,7 @@ void MainTest(const ProgramDesc& prog, int conv_count, int pool_count, (*scales)[v] = std::make_pair(false, std::move(tensor)); } - graph->Set(kParamScopeAttr, new framework::Scope*(&scope)); + graph->SetNotOwned(kParamScopeAttr, &scope); auto pass = PassRegistry::Instance().Get("cpu_quantize_pass"); pass->Set("quant_var_scales", scales); @@ -204,6 +208,101 @@ TEST(CpuQuantizePass, do_not_quantize) { 1.0f); } +} // namespace + +namespace { +static const std::initializer_list variable_names_concat = { + "a1", "b1", "a2", "b2", "c", "d"}; + +// a1->Pool1->b1 +// a2->Pool2->b2 +// (b1,b2)->Concat->c +// c->Pool3->d +ProgramDesc BuildProgramDescConcat() { + ProgramDesc prog; + + SetOp(&prog, "pool2d", "Pool1", {"a1"}, {"b1"}, true, false); + SetOp(&prog, "pool2d", "Pool2", {"a2"}, {"b2"}, true, false); + SetOp(&prog, "concat", "Concat", {"b1", "b2"}, {"c"}, true, true); + SetOp(&prog, "pool2d", "Pool3", {"c"}, {"d"}, true, false); + + return prog; +} + +void MainTestConcat(const ProgramDesc& prog, int pool_count, int concat_count, + int quant_count, int dequant_count, int added_nodes_count) { + std::unique_ptr graph(new ir::Graph(prog)); + + // Init scope, as it is used in pass + auto place = paddle::platform::CPUPlace(); + NaiveExecutor exe{place}; + Scope scope; + exe.CreateVariables(prog, 0, true, &scope); + + auto* scales = new VarQuantScale(); + + for (auto& v : variable_names_concat) { + InitTensorHolder(&scope, place, v.c_str()); + LoDTensor tensor; + tensor.Resize({1}); + auto* ptr = tensor.mutable_data(place); + ptr[0] = 2.0; + + (*scales)[v] = std::make_pair(false, std::move(tensor)); + } + + graph->SetNotOwned(kParamScopeAttr, &scope); + + auto pass = PassRegistry::Instance().Get("cpu_quantize_pass"); + pass->Set("quant_var_scales", scales); + + int original_nodes_num = graph->Nodes().size(); + + graph.reset(pass->Apply(graph.release())); + + int current_nodes_num = graph->Nodes().size(); + + int quantize_nodes_count = 0; + int dequantize_nodes_count = 0; + int concat_nodes_count = 0; + int pool2d_nodes_count = 0; + for (auto* node : graph->Nodes()) { + if (node->IsOp()) { + auto* op = node->Op(); + if (op->Type() == "concat") { + concat_nodes_count++; + } else if (op->Type() == "pool2d") { + pool2d_nodes_count++; + } else if (op->Type() == "quantize") { + quantize_nodes_count++; + } else if (op->Type() == "dequantize") { + dequantize_nodes_count++; + } + } + } + EXPECT_EQ(concat_nodes_count, concat_count); + EXPECT_EQ(pool2d_nodes_count, pool_count); + EXPECT_EQ(quantize_nodes_count, quant_count); + EXPECT_EQ(dequantize_nodes_count, dequant_count); + EXPECT_EQ(original_nodes_num + added_nodes_count, current_nodes_num); +} + +TEST(CpuQuantizePass, concat) { + // a1->Pool1->b1 + // a2->Pool2->b2 + // (b1->QUANT1->IN1, b2->QUANT2->IN2)->Concat->c + // c->OUT1->DEQUANT1->Pool3->d + int pool_count = 3; + int concat_count = 1; + int quant_count = 2; + int dequant_count = 1; + int added_nodes_count = 6; + MainTestConcat(BuildProgramDescConcat(), pool_count, concat_count, + quant_count, dequant_count, added_nodes_count); +} + +} // namespace + } // namespace ir } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass.cc b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass.cc index debbbd6440b05c3f8c0db708c8ad5c54e018f725..2270e2b5cc56f7f71a18ef01ad2ddde4f5218d36 100644 --- a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass.cc +++ b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass.cc @@ -14,6 +14,7 @@ // limitations under the License. #include "paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass.h" +#include #include #include #include "paddle/fluid/platform/enforce.h" @@ -81,12 +82,10 @@ void CPUQuantizeSquashPass::Squash( auto quant_out_var_name = quant_out->Name(); auto next_op_inputs = next_op_desc->InputNames(); for (const auto& name : next_op_inputs) { - auto var_name = next_op_desc->Input(name)[0]; - if (var_name.compare(quant_out_var_name) == 0) { - next_op_desc->SetInput( - name, std::vector({dequant_in->Name()})); - break; - } + auto input_names = next_op_desc->Input(name); + std::replace(input_names.begin(), input_names.end(), quant_out_var_name, + dequant_in->Name()); + next_op_desc->SetInput(name, input_names); } if (keep_dequant) diff --git a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass_tester.cc b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass_tester.cc index fda337066f4d43f88d0082b5bcebc587f0c7652b..057a790ccb3147c6e366322cdb62d4665c946b33 100644 --- a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass_tester.cc +++ b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass_tester.cc @@ -102,8 +102,7 @@ void InitTensorHolder(Scope* scope, const paddle::platform::Place& place, const char* var_name) { auto x = scope->Var(var_name); auto tensor = x->GetMutable(); - tensor->mutable_data(place, proto::VarType::FP32, - ::paddle::memory::Allocator::kDefault, 1); + tensor->mutable_data(place, proto::VarType::FP32, 1); } void MainTest(const ProgramDesc& prog, int removed_nodes_num) { @@ -119,7 +118,7 @@ void MainTest(const ProgramDesc& prog, int removed_nodes_num) { InitTensorHolder(&scope, place, v.c_str()); } - graph->Set(kParamScopeAttr, new framework::Scope*(&scope)); + graph->SetNotOwned(kParamScopeAttr, &scope); auto pass = PassRegistry::Instance().Get("cpu_quantize_squash_pass"); diff --git a/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.cc b/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.cc index 500419e4b7819e576e4e9f2dcc9a01a414519ff8..a2092a5059a7f8de4de59ecc054c88bf888e8318 100644 --- a/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.cc +++ b/paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.h" +#include #include #include @@ -24,6 +25,9 @@ void MKLDNNPlacementPass::ApplyImpl(ir::Graph* graph) const { VLOG(3) << "Applies MKL-DNN placement strategy."; const auto& op_types_list = Get>("mkldnn_enabled_op_types"); + if (!graph->Has("use_mkldnn")) { + graph->Set("use_mkldnn", new bool(true)); + } for (const Node* n : graph->Nodes()) { if (n->IsOp()) { auto* op = n->Op(); diff --git a/paddle/fluid/framework/ir/multi_devices_graph_pass/CMakeLists.txt b/paddle/fluid/framework/ir/multi_devices_graph_pass/CMakeLists.txt index 096428e58ab17deda14e70229ef033dbdd7bd04b..4cdb6a7d30882d095a2666ccc45ed7716954c37c 100644 --- a/paddle/fluid/framework/ir/multi_devices_graph_pass/CMakeLists.txt +++ b/paddle/fluid/framework/ir/multi_devices_graph_pass/CMakeLists.txt @@ -14,3 +14,4 @@ cc_library(sequential_execution_pass SRCS sequential_execution_pass.cc DEPS grap cc_library(fuse_all_reduce_op_pass SRCS fuse_all_reduce_op_pass.cc DEPS graph graph_helper fused_all_reduce_op_handle) cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS all_reduce_op_handle graph graph_helper pass) +cc_library(backward_optimizer_op_deps_pass SRCS backward_optimizer_op_deps_pass.cc DEPS graph graph_helper pass) diff --git a/paddle/fluid/framework/ir/multi_devices_graph_pass/all_reduce_deps_pass.cc b/paddle/fluid/framework/ir/multi_devices_graph_pass/all_reduce_deps_pass.cc index 314f8c0424d5fd9b9908f462ccf2227e1dd983db..1019c4f842740b6903ab5c13a936aa62f6b0a04d 100644 --- a/paddle/fluid/framework/ir/multi_devices_graph_pass/all_reduce_deps_pass.cc +++ b/paddle/fluid/framework/ir/multi_devices_graph_pass/all_reduce_deps_pass.cc @@ -22,6 +22,7 @@ #include "paddle/fluid/framework/details/all_reduce_op_handle.h" #include "paddle/fluid/framework/details/container_cast.h" +#include "paddle/fluid/framework/details/fused_all_reduce_op_handle.h" #include "paddle/fluid/framework/details/multi_devices_helper.h" #include "paddle/fluid/framework/ir/graph.h" #include "paddle/fluid/framework/ir/graph_helper.h" @@ -35,9 +36,20 @@ namespace ir { class AllReduceDepsPass : public ir::Pass { protected: void ApplyImpl(ir::Graph* graph) const override { - std::vector all_reduce_op_handles = + std::vector all_reduce_op_handles = GetSortedAllReduceOps(*graph); +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) + auto use_hierarchical_allreduce = + Get(details::kUseHierarchicalAllReduce); + for (size_t i = 0; i < all_reduce_op_handles.size(); ++i) { + auto op_handle = + dynamic_cast(all_reduce_op_handles[i]); + PADDLE_ENFORCE(op_handle, "op_handle must be NCCLOpHandleBase"); + op_handle->SetRunEnv(i, use_hierarchical_allreduce); + } +#endif + for (size_t i = 1; i < all_reduce_op_handles.size(); ++i) { auto* dep_var = new details::DummyVarHandle(graph->CreateControlDepVar()); graph->Get(details::kGraphDepVars) @@ -51,13 +63,12 @@ class AllReduceDepsPass : public ir::Pass { } } - std::vector GetSortedAllReduceOps( + std::vector GetSortedAllReduceOps( const ir::Graph& graph) const { - std::vector all_reduce_op_handles; + std::vector all_reduce_op_handles; std::unordered_map pending_ops; std::unordered_set ready_ops; std::unordered_set next_ready_ops; - auto op_handles = ir::FilterByNodeWrapper(graph); size_t num_of_ops = op_handles.size(); for (details::OpHandleBase* op : op_handles) { @@ -95,13 +106,16 @@ class AllReduceDepsPass : public ir::Pass { void GetSortedAllReduceOps( const std::unordered_set& ready_ops, - std::vector* all_reduce_op_handles) const { - std::vector current_all_reduce_op_handles; + std::vector* all_reduce_op_handles) const { + std::vector current_all_reduce_op_handles; for (auto& op_handle : ready_ops) { auto all_reduce_op_handle = dynamic_cast(op_handle); - if (all_reduce_op_handle) { - current_all_reduce_op_handles.emplace_back(all_reduce_op_handle); + auto fused_all_reduce_op_handle = + dynamic_cast(op_handle); + + if (all_reduce_op_handle || fused_all_reduce_op_handle) { + current_all_reduce_op_handles.emplace_back(op_handle); } } @@ -110,8 +124,8 @@ class AllReduceDepsPass : public ir::Pass { // Sort the current_all_reduce_op_handles according to the name of input. sort(current_all_reduce_op_handles.begin(), current_all_reduce_op_handles.end(), - [](const details::AllReduceOpHandle* left, - const details::AllReduceOpHandle* right) -> bool { + [](const details::OpHandleBase* left, + const details::OpHandleBase* right) -> bool { auto left_in_vars = details::DynamicCast(left->Inputs()); auto right_in_vars = @@ -126,9 +140,9 @@ class AllReduceDepsPass : public ir::Pass { current_all_reduce_op_handles.end()); } - void DebugString(const ir::Graph& graph, - const std::vector& - all_reduce_op_handles) const { + void DebugString( + const ir::Graph& graph, + const std::vector& all_reduce_op_handles) const { // get vars order std::map> vars = GetSoredGradientsFromStaleProgram(graph); diff --git a/paddle/fluid/framework/ir/multi_devices_graph_pass/fuse_all_reduce_op_pass.cc b/paddle/fluid/framework/ir/multi_devices_graph_pass/fuse_all_reduce_op_pass.cc index a2b4c37ab4ace84a48fb428131fc9f92b9d866c3..abfaf1b8d201450ca211911fe4b527948b4ac7e4 100644 --- a/paddle/fluid/framework/ir/multi_devices_graph_pass/fuse_all_reduce_op_pass.cc +++ b/paddle/fluid/framework/ir/multi_devices_graph_pass/fuse_all_reduce_op_pass.cc @@ -34,7 +34,8 @@ class FuseAllReduceOpPass : public ir::Pass { auto &places = Get>(details::kPlaces); auto &local_scopes = Get>(details::kLocalScopes); #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - auto *nccl_ctxs = &Get(details::kNCCLCtxs); + auto *multi_nccl_ctxs = + &Get(details::kNCCLCtxs); #endif std::unordered_set grads; @@ -94,7 +95,7 @@ class FuseAllReduceOpPass : public ir::Pass { } #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) InsertFusedAllReduce(places, local_scopes, group_size, - group_all_reduce_ops, nccl_ctxs, &result); + group_all_reduce_ops, multi_nccl_ctxs, &result); #else InsertFusedAllReduce(places, local_scopes, group_size, group_all_reduce_ops, &result); @@ -107,7 +108,7 @@ class FuseAllReduceOpPass : public ir::Pass { const size_t num_of_all_reduce, const std::vector &all_reduce_ops, #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - const platform::NCCLContextMap *nccl_ctxs, + const platform::NCCLCommunicator *multi_nccl_ctxs, #endif ir::Graph *result) const { std::vector inputs; @@ -135,7 +136,7 @@ class FuseAllReduceOpPass : public ir::Pass { #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places, - local_scopes, nccl_ctxs, result); + local_scopes, multi_nccl_ctxs, result); #else CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places, local_scopes, result); @@ -150,13 +151,13 @@ class FuseAllReduceOpPass : public ir::Pass { const std::vector &places, const std::vector &local_scopes, #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - const platform::NCCLContextMap *nccl_ctxs, + const platform::NCCLCommunicator *multi_nccl_ctxs, #endif ir::Graph *result) const { #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) auto *op_handle = new details::FusedAllReduceOpHandle( result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation), - local_scopes, places, num_of_all_reduce, nccl_ctxs); + local_scopes, places, num_of_all_reduce, multi_nccl_ctxs); #else auto *op_handle = new details::FusedAllReduceOpHandle( result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation), @@ -172,7 +173,7 @@ class FuseAllReduceOpPass : public ir::Pass { } #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - if (!nccl_ctxs) { + if (!multi_nccl_ctxs) { SetCommunicationContext(places, op_handle); } #else diff --git a/paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.cc b/paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.cc index a4cb0599ac4dd061836ff5d4e64a94ad56c72da5..d6d9c8bb891807e0a229959b00479482fe544e7a 100644 --- a/paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.cc +++ b/paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.cc @@ -157,7 +157,11 @@ void MultiDevSSAGraphBuilderBase::Init() const { local_scopes_ = Get>(details::kLocalScopes); strategy_ = Get(kStrategy); #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - nccl_ctxs_ = &Get(details::kNCCLCtxs); + multi_nccl_ctxs_ = &Get(details::kNCCLCtxs); + nccl_ctxs_ = nullptr; + if (multi_nccl_ctxs_) { + nccl_ctxs_ = multi_nccl_ctxs_->DefaultFlatCtx(); + } #endif PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size()); } @@ -460,20 +464,20 @@ void MultiDevSSAGraphBuilderBase::CreateAllReduceOp(ir::Graph *result, result->Get(kGraphOps).emplace_back( new details::SparseAllReduceOpHandle( result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation), - scopes, places, nccl_ctxs_, is_encoded, + scopes, places, multi_nccl_ctxs_, is_encoded, static_cast(strategy_.trainers_endpoints_.size()) * places_.size())); } else { result->Get(kGraphOps).emplace_back( new details::AllReduceOpHandle( result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation), - scopes, places, nccl_ctxs_)); + scopes, places, multi_nccl_ctxs_)); } #elif defined(PADDLE_WITH_CUDA) && !defined(_WIN32) result->Get(kGraphOps).emplace_back( new details::AllReduceOpHandle( result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation), - scopes, places, nccl_ctxs_)); + scopes, places, multi_nccl_ctxs_)); #else result->Get(kGraphOps).emplace_back( new details::AllReduceOpHandle( diff --git a/paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h b/paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h index 3434d45f1420f90eb14da73e8246621ca885fbb1..9b36d231081d4922419881fd115b3ca347d7d064 100644 --- a/paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h +++ b/paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h @@ -96,7 +96,8 @@ class MultiDevSSAGraphBuilderBase : public ir::Pass { size_t device_id) const; #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - mutable platform::NCCLContextMap *nccl_ctxs_; + mutable platform::NCCLContextMap *nccl_ctxs_{nullptr}; + mutable platform::NCCLCommunicator *multi_nccl_ctxs_{nullptr}; #endif mutable std::string loss_var_name_; @@ -130,7 +131,7 @@ class AsyncSSAGraphBuilder : public MultiDevSSAGraphBuilderBase { bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const override { if (node->Op()->Type() == "recv") { VLOG(1) << "set recv op do_not_run to true"; - node->Op()->SetAttr("do_not_run", true); + node->Op()->SetAttr("do_not_run", 1); node->Op()->Flush(); } else if (node->Name() == "lookup_table" || node->Name() == "nce" || node->Name() == "hierarchical_sigmoid") { diff --git a/paddle/fluid/framework/ir/quant_conv2d_dequant_fuse_pass.cc b/paddle/fluid/framework/ir/quant_conv2d_dequant_fuse_pass.cc index 017e3ef234c95da44bcfb6858c06a48aa973164b..62fba440ed4c5ca0cf57e3377bc1c5d5d79d8f3f 100644 --- a/paddle/fluid/framework/ir/quant_conv2d_dequant_fuse_pass.cc +++ b/paddle/fluid/framework/ir/quant_conv2d_dequant_fuse_pass.cc @@ -25,16 +25,20 @@ namespace framework { namespace ir { void RunQuantDequant(ir::Graph* graph, Scope* scope, int times, - const std::string& op_type, - const std::string& quant_type) { + const std::string& op_type, const std::string& quant_type, + const std::string& dequant_type) { const std::string pattern_name = "quant_dequant_fuse"; - // FusePassBase::Init(pattern_name, graph); - const int kNumFields = 5; + int kNumFields = 5; const int kQuantizedWeightOffset = 0; const int kQuantizedOpOffset = 1; const int kQuantizedOpOutOffset = 2; const int kDequantOpOffset = 3; const int kDequantOpOutOffset = 4; + const int kDequantOpWeightScaleOffset = 5; + + if (dequant_type == "fake_channel_wise_dequantize_max_abs") { + kNumFields += 1; + } GraphPatternDetector gpd; auto* x = gpd.mutable_pattern() @@ -42,22 +46,14 @@ void RunQuantDequant(ir::Graph* graph, Scope* scope, int times, ->assert_is_op_input(quant_type, "X") ->AsInput(); - std::string quantized_op_type = ""; + std::string quantized_op_type = op_type; std::string weight_name = ""; - if (op_type == "conv2d") { - quantized_op_type = "conv2d"; - weight_name = "Filter"; - } else if (op_type == "depthwise_conv2d") { - quantized_op_type = "depthwise_conv2d"; - weight_name = "Filter"; - } else if (op_type == "conv2d_fusion") { - quantized_op_type = "conv2d_fusion"; + if (op_type == "conv2d" || op_type == "depthwise_conv2d" || + op_type == "conv2d_fusion") { weight_name = "Filter"; } else if (op_type == "mul") { - quantized_op_type = "mul"; weight_name = "Y"; } else if (op_type == "fc") { - quantized_op_type = "fc"; weight_name = "W"; } else { PADDLE_ENFORCE( @@ -66,7 +62,7 @@ void RunQuantDequant(ir::Graph* graph, Scope* scope, int times, } patterns::QuantDequantOpFuse pattern(gpd.mutable_pattern(), pattern_name); - pattern(x, quantized_op_type, weight_name, times, quant_type); + pattern(x, quantized_op_type, weight_name, times, quant_type, dequant_type); auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph, Graph* g) { @@ -91,6 +87,10 @@ void RunQuantDequant(ir::Graph* graph, Scope* scope, int times, subgraph.at(pattern.GetPDNode("dequant_op" + std::to_string(i)))); nodes.push_back( subgraph.at(pattern.GetPDNode("dequant_op_out" + std::to_string(i)))); + if (dequant_type == "fake_channel_wise_dequantize_max_abs") { + nodes.push_back(subgraph.at( + pattern.GetPDNode("dequant_channel_scale" + std::to_string(i)))); + } } int bit_length = boost::get(quant_op->Op()->GetAttr("bit_length")); @@ -107,10 +107,31 @@ void RunQuantDequant(ir::Graph* graph, Scope* scope, int times, std::unordered_set delete_nodes; for (int i = 0; i < times; i++) { - float max_range = boost::get( - nodes[i * kNumFields + kDequantOpOffset]->Op()->GetAttr("max_range")); - float weight_scale = (range * range) / max_range; + std::vector weight_scale; + + // Get weight scale from dequant op. + if (dequant_type == "fake_channel_wise_dequantize_max_abs") { + auto scales_name = + nodes[i * kNumFields + kDequantOpOffset]->Op()->Input("Scales"); + PADDLE_ENFORCE(scales_name.size() == 2); + const LoDTensor& channel_scale_tensor = + scope->FindVar(scales_name[0])->Get(); + PADDLE_ENFORCE( + paddle::platform::is_cpu_place(channel_scale_tensor.place())); + const float* channel_scale_data = channel_scale_tensor.data(); + for (int i = 0; i < channel_scale_tensor.numel(); i++) { + weight_scale.push_back(channel_scale_data[i]); + } + delete_nodes.insert( + nodes[i * kNumFields + kDequantOpWeightScaleOffset]); + } else { + float max_range = boost::get( + nodes[i * kNumFields + kDequantOpOffset]->Op()->GetAttr( + "max_range")); + weight_scale.push_back((range * range) / max_range); + } + // create new op_desc auto base_op_desc = *nodes[i * kNumFields + kQuantizedOpOffset]->Op()->Proto(); std::string new_input = input_node->Name(); @@ -141,6 +162,7 @@ void RunQuantDequant(ir::Graph* graph, Scope* scope, int times, IR_NODE_LINK_TO(input_node, new_op); IR_NODE_LINK_TO(nodes[i * kNumFields + kQuantizedWeightOffset], new_op); IR_NODE_LINK_TO(new_op, nodes[i * kNumFields + kDequantOpOutOffset]); + delete_nodes.insert(nodes[i * kNumFields + kQuantizedOpOffset]); delete_nodes.insert(nodes[i * kNumFields + kQuantizedOpOutOffset]); delete_nodes.insert(nodes[i * kNumFields + kDequantOpOffset]); @@ -160,16 +182,19 @@ void QuantDequantFusePass::ApplyImpl(ir::Graph* graph) const { const std::string pattern_name = "quant_dequant_fuse"; FusePassBase::Init(pattern_name, graph); + std::unordered_set dequant_types = { + "fake_dequantize_max_abs", "fake_channel_wise_dequantize_max_abs"}; std::unordered_set quant_types = { "fake_quantize_range_abs_max", "fake_quantize_moving_average_abs_max"}; - std::unordered_set quantized_op_types = {"conv2d", "mul", "depthwise_conv2d"}; auto* scope = param_scope(); - for (auto& quant_type : quant_types) { - for (auto& op_type : quantized_op_types) { - for (int i = 6; i >= 1; i--) { - RunQuantDequant(graph, scope, i, op_type, quant_type); + for (auto& dequant_type : dequant_types) { + for (auto& quant_type : quant_types) { + for (auto& op_type : quantized_op_types) { + for (int i = 6; i >= 1; i--) { + RunQuantDequant(graph, scope, i, op_type, quant_type, dequant_type); + } } } } diff --git a/paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.cc b/paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.cc index 3fd368741fb09d41351a97c5e9cf1a5436f350d0..556d28a42ae8d664712417add43732cb57f67355 100644 --- a/paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.cc +++ b/paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.cc @@ -43,11 +43,11 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope) { op_desc.SetAttr("contextStart", seqconv->Op()->GetAttr("contextStart")); op_desc.SetAttr("contextStride", seqconv->Op()->GetAttr("contextStride")); PADDLE_ENFORCE(graph->Has(kParamScopeAttr)); - auto* scope = graph->Get(kParamScopeAttr); + auto& scope = graph->Get(kParamScopeAttr); const std::string ColMat = patterns::UniqueKey("SeqConvColMat"); op_desc.SetOutput("ColMat", {ColMat}); op_desc.SetOutput("Out", {relu_out->Name()}); - scope->Var(ColMat)->GetMutable(); + scope.Var(ColMat)->GetMutable(); auto* op = graph->CreateOpNode(&op_desc); IR_NODE_LINK_TO(input, op); diff --git a/paddle/fluid/framework/lod_tensor.cc b/paddle/fluid/framework/lod_tensor.cc index 2b4683f9e778593852029ec7e9ada7390e915e8b..9883a1940567fb5f5e6ce1eed7774c7d4a90dc9e 100644 --- a/paddle/fluid/framework/lod_tensor.cc +++ b/paddle/fluid/framework/lod_tensor.cc @@ -53,32 +53,8 @@ std::ostream &operator<<(std::ostream &os, const LoD &lod) { } std::ostream &operator<<(std::ostream &os, const LoDTensor &t) { - if (!platform::is_cpu_place(t.place())) { - LoDTensor cpu_tensor; - cpu_tensor.set_lod(t.lod()); - framework::TensorCopy(t, platform::CPUPlace(), &cpu_tensor); - platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance(); - auto &dev_ctx = *pool.Get(t.place()); - dev_ctx.Wait(); - - os << cpu_tensor; - return os; - } - - os << "dim: " << t.dims() << "\n"; - os << "lod: " << t.lod() << "\n"; - - // only print first ten elements - int64_t size = t.numel() < 10 ? t.numel() : 10; - for (int64_t i = 0; i < size; ++i) { - if (t.type() == proto::VarType::FP32) { - os << t.data()[i] << " "; - } else if (t.type() == proto::VarType::INT64) { - os << t.data()[i] << " "; - } else { - PADDLE_THROW("LoDTensor data type not in [float, int64_t]"); - } - } + os << "\tlod: " << t.lod() << "\n"; + os << static_cast(t) << "\n"; return os; } diff --git a/paddle/fluid/framework/lod_tensor_test.cc b/paddle/fluid/framework/lod_tensor_test.cc index 15928c18d38b8a513b00f993b57faab43978bf53..d1554113bc366f38d1cfd7603e2848f618794d9f 100644 --- a/paddle/fluid/framework/lod_tensor_test.cc +++ b/paddle/fluid/framework/lod_tensor_test.cc @@ -28,12 +28,14 @@ namespace framework { TEST(LoD, PrintLoDTensor) { LoDTensor tensor1; + tensor1.Resize({2}); tensor1.mutable_data(platform::CPUPlace()); tensor1.data()[0] = 0.2; tensor1.data()[1] = 0.5; LOG(INFO) << tensor1; LoDTensor tensor2; + tensor2.Resize({2}); tensor2.mutable_data(platform::CPUPlace()); tensor2.data()[0] = 1; tensor2.data()[1] = 2; diff --git a/paddle/fluid/framework/op_desc.cc b/paddle/fluid/framework/op_desc.cc index 8d3864c6b3da5500bb9017437c3cd16f06494abb..1ea93b7638a85e67bcc85a0c0e130d636938d6c5 100644 --- a/paddle/fluid/framework/op_desc.cc +++ b/paddle/fluid/framework/op_desc.cc @@ -13,13 +13,12 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/framework/op_desc.h" -#include #include #include #include // NOLINT #include #include -#include +#include "glog/logging.h" #include "paddle/fluid/framework/block_desc.h" #include "paddle/fluid/framework/op_proto_maker.h" #include "paddle/fluid/framework/operator.h" diff --git a/paddle/fluid/framework/op_desc.h b/paddle/fluid/framework/op_desc.h index f06f67dcc3d32c87e93a3df44643f71d61689faa..dedaf24364703877a4cacb23a27550b54dad53f8 100644 --- a/paddle/fluid/framework/op_desc.h +++ b/paddle/fluid/framework/op_desc.h @@ -33,7 +33,7 @@ class OpDesc { OpDesc(const std::string &type, const VariableNameMap &inputs, const VariableNameMap &outputs, const AttributeMap &attrs); - explicit OpDesc(const proto::OpDesc &desc, BlockDesc *block = nullptr); + OpDesc(const proto::OpDesc &desc, BlockDesc *block); explicit OpDesc(BlockDesc *block) : block_(block) {} @@ -42,7 +42,6 @@ class OpDesc { void CopyFrom(const OpDesc &op_desc); proto::OpDesc *Proto(); - const proto::OpDesc &ReadonlyProto() const { return desc_; } std::string Type() const { return desc_.type(); } diff --git a/paddle/fluid/framework/operator.cc b/paddle/fluid/framework/operator.cc index dab35bae4d524182c6534a9deb83076d69009bdd..8d4623468b98d40ce52f88218a7927803ef494ca 100644 --- a/paddle/fluid/framework/operator.cc +++ b/paddle/fluid/framework/operator.cc @@ -39,10 +39,6 @@ DEFINE_int32(inner_op_parallelism, 0, "number of threads for inner op"); namespace paddle { namespace framework { -OpDuppy op_duppy; -Scope scope_duppy; -RuntimeContext runtime_context_duppy({}, {}); - std::vector> kKernelPriority = { std::make_tuple(platform::CUDAPlace(0), LibraryType::kCUDNN), std::make_tuple(platform::CUDAPlace(0), LibraryType::kPlain), @@ -888,8 +884,6 @@ void OperatorWithKernel::RunImpl(const Scope& scope, // result of HasAttr. if (!enable_cache_runtime_context && HasAttr(kEnableCacheRuntimeContext)) enable_cache_runtime_context = true; - if (!enable_cache_expected_kernel && HasAttr(kEnableCacheExpectedKernel)) - enable_cache_expected_kernel = true; if (!all_kernels_must_compute_runtime_shape && HasAttr(kAllKernelsMustComputeRuntimeShape)) all_kernels_must_compute_runtime_shape = true; @@ -898,9 +892,12 @@ void OperatorWithKernel::RunImpl(const Scope& scope, RunImpl(scope, place, &ctx); } else { const Scope* cur_scope = &scope; - if (!runtime_ctx_ || pre_scope_ != cur_scope) { - runtime_ctx_.reset(new RuntimeContext(Inputs(), Outputs(), scope)); - pre_scope_ = cur_scope; + if (runtime_ctx_.get() == nullptr || pre_scope_ != cur_scope) { + std::lock_guard lock(cache_update_mutex_); + if (runtime_ctx_.get() == nullptr || pre_scope_ != cur_scope) { + runtime_ctx_.reset(new RuntimeContext(Inputs(), Outputs(), scope)); + pre_scope_ = cur_scope; + } } RunImpl(scope, place, runtime_ctx_.get()); } @@ -912,7 +909,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope, platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance(); auto* dev_ctx = pool.Get(place); - if (!enable_cache_expected_kernel || !kernel_type_) { + if (kernel_type_.get() == nullptr || kernel_func_.get() == nullptr) { ChooseKernel(*runtime_ctx, scope, place); } @@ -1000,8 +997,11 @@ void OperatorWithKernel::ChooseKernel(const RuntimeContext& ctx, KernelTypeToString(expected_kernel_key)); } - kernel_type_.reset(new OpKernelType(expected_kernel_key)); - kernel_func_.reset(new OpKernelFunc(kernel_iter->second)); + std::lock_guard lock(cache_update_mutex_); + if (kernel_type_.get() == nullptr || kernel_func_.get() == nullptr) { + kernel_type_.reset(new OpKernelType(expected_kernel_key)); + kernel_func_.reset(new OpKernelFunc(kernel_iter->second)); + } } void OperatorWithKernel::TransferInplaceVarsBack( @@ -1027,7 +1027,6 @@ Scope* OperatorWithKernel::PrepareData( std::vector* transfered_inplace_vars, RuntimeContext* ctx) const { Scope* new_scope = nullptr; - if (!need_prepare_data_) return new_scope; std::unordered_set no_buffer_ins; if (info_) { @@ -1120,10 +1119,6 @@ Scope* OperatorWithKernel::PrepareData( SetTensorToVariable(*var, out, trans_var); } } - // If new_scope = nullptr, it means that for each input of this Op, there is - // no TransformData. Thus, PrepareData could be skipped at the rest iterations - // of this Op's execution to save the elapsed time. - if (!new_scope) need_prepare_data_ = false; return new_scope; } @@ -1147,7 +1142,7 @@ proto::VarType::Type OperatorWithKernel::IndicateDataType( t = &(var->Get().value()); } if (t != nullptr) { - PADDLE_ENFORCE(t->IsInitialized(), "Input %s(%lu)is not initialized", + PADDLE_ENFORCE(t->IsInitialized(), "Input %s(%lu) is not initialized", input.first, i); proto::VarType::Type tmp = t->type(); PADDLE_ENFORCE( diff --git a/paddle/fluid/framework/operator.h b/paddle/fluid/framework/operator.h index 8f301c6ebce124aea69532fadc6dc2189c395d72..8e158e93063cb7620440b0af8433c0baa02eab22 100644 --- a/paddle/fluid/framework/operator.h +++ b/paddle/fluid/framework/operator.h @@ -17,6 +17,7 @@ limitations under the License. */ #include #include #include +#include // NOLINT #include #include #include @@ -70,12 +71,6 @@ constexpr char kNewGradSuffix[] = "@NEWGRAD@"; /// this Op's execution to save the elapsed time. constexpr char kEnableCacheRuntimeContext[] = "@ENABLE_CACHE_RUNTIME_CONTEXT@"; -/// If an Op has attribtue kEnableCacheExpectedKernel, it means that in a same -/// name scope and same place, since the expected kerenl of this Op does not -/// change in the execution, it could be recorded only at the first iteration of -/// this Op's execution to save the elapsed time. -constexpr char kEnableCacheExpectedKernel[] = "@ENABLE_CACHE_EXPECTED_KERNEL@"; - /// If an Op has this attribute, all its kernels should calculate output /// variable's shape in the corresponding Compute() function. And /// OperatorWithKernel::RunImpl() would skip call this Op's InferShape() @@ -232,18 +227,6 @@ using OpKernelConfigsMap = std::unordered_map, OpKernelType::Hash>; -class OpDuppy : public OperatorBase { - public: - OpDuppy() : OperatorBase("duppy", {}, {}, {}) {} - - void RunImpl(const Scope& scope, - const platform::Place& place) const override {} -}; - -extern OpDuppy op_duppy; -extern Scope scope_duppy; -extern RuntimeContext runtime_context_duppy; - class ExecutionContext { public: ExecutionContext(const OperatorBase& op, const Scope& scope, @@ -256,13 +239,6 @@ class ExecutionContext { ctx_(ctx), kernel_configs_(configs) {} - explicit ExecutionContext(const platform::DeviceContext& device_context) - : op_(op_duppy), - scope_(scope_duppy), - device_context_(device_context), - ctx_(runtime_context_duppy), - kernel_configs_(nullptr) {} - const OperatorBase& op() const { return op_; } const Scope& scope() const { return scope_; } @@ -390,9 +366,6 @@ class ExecutionContext { auto shared_allocation = std::shared_ptr( allocation_ptr, deleter); - PADDLE_ENFORCE( - dynamic_cast(allocation_ptr) != nullptr, - "The AllocationPtr must be TemporaryAllocation."); PADDLE_ENFORCE_GE(allocation_ptr->size(), framework::product(dim) * sizeof(T)); @@ -404,12 +377,12 @@ class ExecutionContext { } template - T& GetKernelConfig(int idx) const { + T& GetKernelConfig(size_t idx) const { PADDLE_ENFORCE( kernel_configs_ && kernel_configs_->size() > static_cast(idx), - "%s selected kernel doesn't have kernel config %lu <= %d", + "%s selected kernel doesn't have kernel config %lu <= %lu", op_.Type().c_str(), kernel_configs_->size(), idx); - return *boost::get>(kernel_configs_->at(idx)); + return *boost::get>((*kernel_configs_)[idx]); } private: @@ -526,10 +499,9 @@ class OperatorWithKernel : public OperatorBase { mutable std::unique_ptr kernel_func_; mutable std::unique_ptr runtime_ctx_; mutable const Scope* pre_scope_ = nullptr; - mutable bool need_prepare_data_ = true; mutable bool enable_cache_runtime_context = false; - mutable bool enable_cache_expected_kernel = false; mutable bool all_kernels_must_compute_runtime_shape = false; + mutable std::mutex cache_update_mutex_; }; extern bool OpSupportGPU(const std::string& op_type); diff --git a/paddle/fluid/framework/operator_kernel_configs.h b/paddle/fluid/framework/operator_kernel_configs.h index c520c222350ceeef246dae756a7157872ae087fa..a350b8957d91ea21375e1942af2968277b10833e 100644 --- a/paddle/fluid/framework/operator_kernel_configs.h +++ b/paddle/fluid/framework/operator_kernel_configs.h @@ -103,7 +103,7 @@ TAlgorithm AlgorithmsCache::GetAlgorithm( ++search_times_; return algo; } - TAlgorithm algo; + TAlgorithm algo{}; int64_t min = static_cast(INT_MAX); for (const auto& m : hash_) { if (m.first < min) { diff --git a/paddle/fluid/framework/parallel_executor.cc b/paddle/fluid/framework/parallel_executor.cc index f400e8a5cc031cb0982860a6c2c1c9aba77f35dc..8853ee3bd18c9bf26dd9e79f0bb11075a4ef5e38 100644 --- a/paddle/fluid/framework/parallel_executor.cc +++ b/paddle/fluid/framework/parallel_executor.cc @@ -94,6 +94,113 @@ class ParallelExecutorPrivate { } } +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) + void InitNCCLCtxs(framework::Scope *scope, const BuildStrategy &bst) { + VLOG(1) << "nccl comm num:" << bst.nccl_comm_num_ << ", nranks:" << nranks_ + << ", num_trainers:" << bst.num_trainers_ + << ", trainer_id:" << bst.trainer_id_; + + if (bst.use_hierarchical_allreduce_) { + VLOG(1) << ", use_hierarchical_allreduce:" + << bst.use_hierarchical_allreduce_ << ", inter_trainers_num:" + << bst.hierarchical_allreduce_inter_nranks_ + << ", exter_trainers_num:" + << bst.hierarchical_allreduce_exter_nranks_; + } + + std::vector flat_nccl_ids; + if (nranks_ == 1) { + // FIXME(gongwb): need not to create ncclid when nranks==1 + nccl_ctxs_->InitFlatCtxs(places_, flat_nccl_ids, bst.num_trainers_, + bst.trainer_id_); + return; + } + + if (bst.enable_parallel_graph_) { + VLOG(1) << "use only one ncclid in pg model"; + + ncclUniqueId *nccl_id = nullptr; + + std::string var_name = platform::GetFlatNCCLVarName(0); + auto nccl_id_var = scope->FindVar(var_name); + if (nccl_id_var) { + nccl_id = nccl_id_var->GetMutable(); + } else { + nccl_id = new ncclUniqueId(); + PADDLE_ENFORCE(platform::dynload::ncclGetUniqueId(nccl_id)); + } + + flat_nccl_ids.push_back(nccl_id); + + nccl_ctxs_->InitFlatCtxs(places_, flat_nccl_ids, bst.num_trainers_, + bst.trainer_id_); + VLOG(1) << "init bst nccl context complete!"; + return; + } + + // num_trainers ==1 && places > 1 + if (bst.num_trainers_ == 1) { + nccl_ctxs_->InitFlatCtxs(places_, flat_nccl_ids, bst.num_trainers_, + bst.trainer_id_); + return; + } + + for (int i = 0; i < static_cast(bst.nccl_comm_num_); i++) { + std::string var_name = platform::GetFlatNCCLVarName(i); + auto nccl_id_var = scope->FindVar(var_name); + PADDLE_ENFORCE(nccl_id_var, "can't find %s nccl_id_var", var_name); + auto nccl_id = nccl_id_var->GetMutable(); + flat_nccl_ids.push_back(nccl_id); + } + + nccl_ctxs_->InitFlatCtxs(places_, flat_nccl_ids, bst.num_trainers_, + bst.trainer_id_); + + if (bst.use_hierarchical_allreduce_) { + std::vector inter_nccl_ids; + for (int i = 0; i < static_cast(bst.nccl_comm_num_); i++) { + std::string var_name = platform::GetHierarchicalInterNCCLVarName(i); + auto nccl_id_var = scope->FindVar(var_name); + PADDLE_ENFORCE(nccl_id_var, "can't find %s nccl_id_var", var_name); + auto inter_nccl_id = nccl_id_var->GetMutable(); + inter_nccl_ids.push_back(inter_nccl_id); + } + + std::vector exter_nccl_ids; + for (int i = 0; i < static_cast(bst.nccl_comm_num_); i++) { + std::string var_name = platform::GetHierarchicalExterNCCLVarName(i); + auto nccl_id_var = scope->FindVar(var_name); + PADDLE_ENFORCE(nccl_id_var, "can't find %s nccl_id_var", var_name); + auto nccl_id = nccl_id_var->GetMutable(); + exter_nccl_ids.push_back(nccl_id); + } + + nccl_ctxs_->InitHierarchicalCtxs( + places_, inter_nccl_ids, exter_nccl_ids, bst.num_trainers_, + bst.trainer_id_, bst.hierarchical_allreduce_inter_nranks_, + bst.hierarchical_allreduce_exter_nranks_); + } + } + + void InitOrGetNCCLCommunicator(framework::Scope *scope, + const BuildStrategy &bst) { + const std::string var_name = "NCCLCommunicator"; + auto var = scope->FindVar(var_name); + if (var != nullptr) { + PADDLE_ENFORCE(var->IsInitialized(), + "if %s exists, it must be initialized", var_name); + VLOG(1) << "find " << var_name + << " in scope, so use it and does not recreate!"; + nccl_ctxs_ = var->GetMutable(); + return; + } + + VLOG(1) << "not find " << var_name << " in scope, so recreate it!"; + nccl_ctxs_ = scope->Var(var_name)->GetMutable(); + InitNCCLCtxs(scope, bst); + } +#endif + BuildStrategy build_strategy_; std::vector places_; std::vector local_scopes_; @@ -101,7 +208,7 @@ class ParallelExecutorPrivate { std::unique_ptr executor_; #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - std::unique_ptr nccl_ctxs_; + platform::NCCLCommunicator *nccl_ctxs_{nullptr}; #endif bool own_local_scope_; bool use_cuda_; @@ -207,12 +314,23 @@ ParallelExecutor::ParallelExecutor(const std::vector &places, member_->use_all_reduce_ = build_strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce; member_->nranks_ = build_strategy.num_trainers_ * places.size(); +#if defined(PADDLE_WITH_CUDA) && defined(_WIN32) + if (member_->use_cuda_) { + PADDLE_ENFORCE(places.size() == 1, "Windows can support Single GPU only."); + } +#endif if (!member_->use_all_reduce_) { PADDLE_ENFORCE(places.size() > 1, "If you set build_strategy.reduce with 'Reduce'," "the number of places must be greater than 1."); } + LOG(WARNING) << string::Sprintf( + "The number of %s, which is used in ParallelExecutor, is %lu. And " + "the Program will be copied %lu copies", + (member_->use_cuda_ ? "CUDAPlace" : "CPUPlace"), places.size(), + places.size()); + // Step 1. Bcast the bcast_vars to devs. // Create local scopes if (local_scopes.empty()) { @@ -251,27 +369,9 @@ ParallelExecutor::ParallelExecutor(const std::vector &places, "Execution which can get better performance," << "you can force it off by env FLAGS_enable_parallel_graph=0"; - if (member_->use_cuda_) { -// Bcast Parameters to all GPUs + if (member_->use_cuda_ && member_->nranks_ > 1) { #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - ncclUniqueId *nccl_id = nullptr; - // gen_nccl_id operator can broadcast the ncclUniqueId for nccl2 collective - // distributed training - auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME); - if (nccl_id_var != nullptr) { - nccl_id = nccl_id_var->GetMutable(); - } - if (build_strategy.enable_parallel_graph_ && member_->nranks_ > 1UL) { - if (nccl_id == nullptr) { - local_nccl_id_.reset(new ncclUniqueId()); - platform::dynload::ncclGetUniqueId(local_nccl_id_.get()); - nccl_id = local_nccl_id_.get(); - } - } - - member_->nccl_ctxs_.reset(new platform::NCCLContextMap( - member_->places_, nccl_id, build_strategy.num_trainers_, - build_strategy.trainer_id_)); + member_->InitOrGetNCCLCommunicator(scope, build_strategy); // Initialize device context's nccl comm, will be used by normal // Operators like sync_batch_norm, and collective ops. @@ -280,25 +380,16 @@ ParallelExecutor::ParallelExecutor(const std::vector &places, // NOTE: NCCL group-calls and non-group-calls can not use the same // NCCL communicator, so for ParallelGraph and Multi-Process mode, re-use // same communicators. - std::unique_ptr dev_nccl_ctxs; - if (nccl_id == nullptr) { - dev_nccl_ctxs.reset(new platform::NCCLContextMap(member_->places_)); - } + auto *nccl_ctxs = + member_->nccl_ctxs_->GetSyncBatchNormCtx(scope, member_->places_); for (size_t dev_id = 0; dev_id < member_->places_.size(); ++dev_id) { platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance(); auto *dev_ctx = static_cast( pool.Get(member_->places_[dev_id])); - if (nccl_id != nullptr) { - auto &nccl_ctx = member_->nccl_ctxs_->at(member_->places_[dev_id]); - dev_ctx->set_nccl_comm(nccl_ctx.comm()); - } else { - auto &nccl_ctx = dev_nccl_ctxs->at(member_->places_[dev_id]); - dev_ctx->set_nccl_comm(nccl_ctx.comm()); - } + auto &nccl_ctx = nccl_ctxs->at(member_->places_[dev_id]); + dev_ctx->set_nccl_comm(nccl_ctx.comm()); } -#else - PADDLE_THROW("Not compiled with CUDA"); #endif } // broadcast parameters from the 0th device to others: @@ -313,10 +404,11 @@ ParallelExecutor::ParallelExecutor(const std::vector &places, } return false; }; - + // Bcast Parameters to all GPUs if (need_broadcast()) { BCastParamsToDevices(bcast_vars, build_strategy.trainer_id_); } + // Startup Program has been run. All local scopes has correct parameters. // Step 2. Convert main_program to SSA form and dependency graph. Also, insert @@ -327,18 +419,18 @@ ParallelExecutor::ParallelExecutor(const std::vector &places, VLOG(3) << "use local async mode"; graph = build_strategy.Apply(graph, {member_->places_[0]}, loss_var_name, {member_->local_scopes_[0]}, 1, - member_->use_cuda_, member_->nccl_ctxs_.get()); + member_->use_cuda_, member_->nccl_ctxs_); for (size_t i = 1; i < member_->places_.size(); ++i) { graphs[i] = build_strategy.Apply(graphs[i], {member_->places_[i]}, loss_var_name, {member_->local_scopes_[i]}, 1, - member_->use_cuda_, member_->nccl_ctxs_.get()); + member_->use_cuda_, member_->nccl_ctxs_); async_graphs[i] = graphs[i]; } } else { graph = build_strategy.Apply(graph, member_->places_, loss_var_name, member_->local_scopes_, member_->nranks_, - member_->use_cuda_, member_->nccl_ctxs_.get()); + member_->use_cuda_, member_->nccl_ctxs_); } #else if (build_strategy.async_mode_) { @@ -471,16 +563,15 @@ void ParallelExecutor::BCastParamsToDevices( PADDLE_ENFORCE_EQ(member_->places_.size(), buffers.size(), "variables' buffer size to bcast NOT equal to places"); { + auto *nccl_ctxs = member_->nccl_ctxs_->DefaultFlatCtx(); platform::NCCLGroupGuard guard; for (size_t i = 0; i < member_->places_.size(); ++i) { - auto &nccl_ctx = member_->nccl_ctxs_->at(member_->places_[i]); + auto &nccl_ctx = nccl_ctxs->at(member_->places_[i]); platform::dynload::ncclBcast(buffers[i], numel, data_type, 0, nccl_ctx.comm_, nccl_ctx.stream()); } - member_->nccl_ctxs_->WaitAll(); + nccl_ctxs->WaitAll(); } -#else - PADDLE_THROW("Not compiled with CUDA"); #endif } else { platform::CPUPlace cpu; @@ -512,6 +603,7 @@ void ParallelExecutor::BCastParamsToDevices( void ParallelExecutor::Run(const std::vector &fetch_tensors, const std::string &fetched_var_name) { + VLOG(3) << "enter ParallelExecutor Run"; #ifdef WITH_GPERFTOOLS if (gProfileStarted) { ProfilerFlush(); @@ -520,8 +612,11 @@ void ParallelExecutor::Run(const std::vector &fetch_tensors, platform::RecordBlock b(0); if (member_->HasGarbageCollectors()) { + platform::RecordEvent event("PrepareGarbageCollectors"); member_->ResetRuntimeReferenceCount(fetch_tensors, fetched_var_name); } + + VLOG(3) << "ParallelExecutor begin to run member_->executor_->Run"; auto fetch_data = member_->executor_->Run(fetch_tensors); *member_->global_scope_->Var(fetched_var_name)->GetMutable() = fetch_data; @@ -546,11 +641,21 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes( const std::unordered_map &tensors) { for (auto pair : tensors) { auto lod_tensors = pair.second.SplitLoDTensor(member_->places_); - PADDLE_ENFORCE_EQ( - member_->places_.size(), lod_tensors.size(), - "The number of samples of current batch is less than the count of " - "devices, currently, it is not allowed. (%d vs %d)", - member_->places_.size(), lod_tensors.size()); + if (member_->places_.size() != lod_tensors.size()) { + bool is_cpu_place = platform::is_cpu_place(member_->places_.front()); + auto error_info = string::Sprintf( + "The number(%d) of samples of " + "current batch is less than the count(%d) of " + "devices(%s), currently, it is not allowed. ", + lod_tensors.size(), member_->places_.size(), + (is_cpu_place ? "CPU" : "GPU")); + if (is_cpu_place) { + error_info += + "You should set the environment variable CPU_NUM in the system " + "to determine the number of devices you need."; + } + PADDLE_THROW(error_info); + } for (size_t j = 0; j < member_->places_.size(); ++j) { // TODO(panxy0718): Do I need to delete this var? auto t = @@ -571,7 +676,9 @@ ParallelExecutor::~ParallelExecutor() { bool ParallelExecutor::EnableParallelGraphExecution( const ir::Graph &graph, const ExecutionStrategy &exec_strategy, const BuildStrategy &build_strategy) const { - if (!FLAGS_enable_parallel_graph) return false; + if (!FLAGS_enable_parallel_graph) { + return false; + } bool enable_parallel_graph = true; @@ -591,11 +698,19 @@ bool ParallelExecutor::EnableParallelGraphExecution( } } - if (!member_->use_all_reduce_ || !member_->use_cuda_) - + if (!member_->use_all_reduce_ || !member_->use_cuda_) { if (build_strategy.enable_sequential_execution_ || - exec_strategy.type_ == ExecutionStrategy::ExecutorType::kExperimental) + exec_strategy.type_ == ExecutionStrategy::ExecutorType::kExperimental) { enable_parallel_graph = false; + } + } + +#ifdef WIN32 + VLOG(1) << "Windows has no support to parallel graph, enable_parallel_graph " + "would be forced to false."; + enable_parallel_graph = false; +#endif + return enable_parallel_graph; } diff --git a/paddle/fluid/framework/parallel_executor.h b/paddle/fluid/framework/parallel_executor.h index 2de6b7f73d2a03a4b9f23b49142f677df6120806..6943fe62b915e0707dfe40ecbda90f61464338cf 100644 --- a/paddle/fluid/framework/parallel_executor.h +++ b/paddle/fluid/framework/parallel_executor.h @@ -87,10 +87,6 @@ class ParallelExecutor { ParallelExecutorPrivate *member_; std::vector> async_graphs_; -#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - std::unique_ptr local_nccl_id_; -#endif }; - } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/pull_dense_worker.cc b/paddle/fluid/framework/pull_dense_worker.cc index c48c7872ec23f6cfaac650b4940752ac9b8fd36c..20d7f98e93695107637107c60f5ef42b8ce9293d 100644 --- a/paddle/fluid/framework/pull_dense_worker.cc +++ b/paddle/fluid/framework/pull_dense_worker.cc @@ -83,28 +83,34 @@ void PullDenseWorker::Stop() { } } +void PullDenseWorker::PullDense(bool force_update) { + pull_dense_status_.resize(0); + for (size_t i = 0; + i < dwp_param_.program_config(0).pull_dense_table_id_size(); ++i) { + uint64_t tid = static_cast( + dwp_param_.program_config(0).pull_dense_table_id(i)); + if (force_update || CheckUpdateParam(tid)) { + fleet_ptr_->PullDenseVarsAsync(*root_scope_, tid, dense_value_names_[tid], + &pull_dense_status_); + ResetThreadVersion(tid); + } + } + if (pull_dense_status_.size() != 0) { + Wait(&pull_dense_status_); + } +} + int PullDenseWorker::Start() { running_ = true; + // before training, we can pull dense from pserver first. + PullDense(true); t_ = std::thread(&PullDenseWorker::Run, this); return 0; } void PullDenseWorker::Run() { while (running_) { - pull_dense_status_.resize(0); - for (size_t i = 0; - i < dwp_param_.program_config(0).pull_dense_table_id_size(); ++i) { - uint64_t tid = static_cast( - dwp_param_.program_config(0).pull_dense_table_id(i)); - if (CheckUpdateParam(tid)) { - fleet_ptr_->PullDenseVarsAsync( - *root_scope_, tid, dense_value_names_[tid], &pull_dense_status_); - ResetThreadVersion(tid); - } - } - if (pull_dense_status_.size() != 0) { - Wait(&pull_dense_status_); - } + PullDense(false); #ifndef _WIN32 usleep(sleep_time_ms_ * 1000); #endif diff --git a/paddle/fluid/framework/tensor.cc b/paddle/fluid/framework/tensor.cc index ea7f8c496a9fc3ff78fce06b69fb21e44e5be9ee..565b7d9d16cb4d048c57b841857390a3dea3ed7a 100644 --- a/paddle/fluid/framework/tensor.cc +++ b/paddle/fluid/framework/tensor.cc @@ -35,7 +35,6 @@ size_t Tensor::memory_size() const { } void* Tensor::mutable_data(platform::Place place, proto::VarType::Type type, - memory::Allocator::Attr attr, size_t requested_size) { type_ = type; PADDLE_ENFORCE_GE(numel(), 0, @@ -50,18 +49,17 @@ void* Tensor::mutable_data(platform::Place place, proto::VarType::Type type, /* some versions of boost::variant don't have operator!= */ if (holder_ == nullptr || !(holder_->place() == place) || holder_->size() < size + offset_) { - holder_ = memory::AllocShared(place, size, attr); + holder_ = memory::AllocShared(place, size); offset_ = 0; } return reinterpret_cast(reinterpret_cast(holder_->ptr()) + offset_); } -void* Tensor::mutable_data(platform::Place place, memory::Allocator::Attr attr, - size_t requested_size) { +void* Tensor::mutable_data(platform::Place place, size_t requested_size) { PADDLE_ENFORCE(this->holder_ != nullptr, "Cannot invoke mutable data if current hold nothing."); - return mutable_data(place, type_, attr, requested_size); + return mutable_data(place, type_, requested_size); } Tensor& Tensor::ShareDataWith(const Tensor& src) { diff --git a/paddle/fluid/framework/tensor.h b/paddle/fluid/framework/tensor.h index f83a1aa49d5e9e6a544fa4f241dadf417078a4e6..1ab75e3325740a30c9233b4cef660a869368112a 100644 --- a/paddle/fluid/framework/tensor.h +++ b/paddle/fluid/framework/tensor.h @@ -80,8 +80,6 @@ class Tensor { template const T* data() const; - const void* raw_data() const { return holder_->ptr(); } - inline bool IsInitialized() const; /** @@ -89,17 +87,12 @@ class Tensor { * @note If not exist, then allocation. */ template - T* mutable_data(platform::Place place, - memory::Allocator::Attr attr = memory::Allocator::kDefault, - size_t requested_size = 0); + T* mutable_data(platform::Place place, size_t requested_size = 0); void* mutable_data(platform::Place place, proto::VarType::Type type, - memory::Allocator::Attr attr = memory::Allocator::kDefault, size_t requested_size = 0); - void* mutable_data(platform::Place place, - memory::Allocator::Attr attr = memory::Allocator::kDefault, - size_t requested_size = 0); + void* mutable_data(platform::Place place, size_t requested_size = 0); /** * @brief Return a pointer to mutable memory block. @@ -111,9 +104,7 @@ class Tensor { * @note If not exist, then allocation. */ template - T* mutable_data(DDim dims, platform::Place place, - memory::Allocator::Attr attr = memory::Allocator::kDefault, - size_t requested_size = 0); + T* mutable_data(DDim dims, platform::Place place, size_t requested_size = 0); /*! Return the dimensions of the memory block. */ const DDim& dims() const; diff --git a/paddle/fluid/framework/tensor_impl.h b/paddle/fluid/framework/tensor_impl.h index 8dabecac8ab42ee0fb6b57048f3a1c8223d0b0b1..a4b1457ad567cf5f1f2788a5c24889c3066c84b0 100644 --- a/paddle/fluid/framework/tensor_impl.h +++ b/paddle/fluid/framework/tensor_impl.h @@ -49,20 +49,17 @@ inline T* Tensor::data() { template inline T* Tensor::mutable_data(DDim dims, platform::Place place, - memory::Allocator::Attr attr, size_t requested_size) { static_assert(std::is_pod::value, "T must be POD"); Resize(dims); - return mutable_data(place, attr, requested_size); + return mutable_data(place, requested_size); } template -inline T* Tensor::mutable_data(platform::Place place, - memory::Allocator::Attr attr, - size_t requested_size) { +inline T* Tensor::mutable_data(platform::Place place, size_t requested_size) { static_assert(std::is_pod::value, "T must be POD"); return reinterpret_cast( - mutable_data(place, DataTypeTrait::DataType, attr, requested_size)); + mutable_data(place, DataTypeTrait::DataType, requested_size)); } inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) { diff --git a/paddle/fluid/framework/tensor_util.cc b/paddle/fluid/framework/tensor_util.cc index a7f09df4917532e7261cee471c711897c8eb3447..33ef3b91866f477910b105b15014854788a070d5 100644 --- a/paddle/fluid/framework/tensor_util.cc +++ b/paddle/fluid/framework/tensor_util.cc @@ -491,5 +491,51 @@ void TensorFromStream(std::istream& is, Tensor* tensor, } } +template +std::ostream& print_tensor(std::ostream& os, const framework::Tensor& tensor) { + auto inspect = tensor.data(); + auto element_num = tensor.numel(); + + os << "\tdata: ["; + if (element_num > 0) { + os << inspect[0]; + for (int j = 1; j < element_num; ++j) { + os << " " << inspect[j]; + } + } + os << "]"; + return os; +} + +std::ostream& operator<<(std::ostream& os, const Tensor& t) { + os << "\tdim: " << t.dims() << "\n"; + os << "\tlayout: " << DataLayoutToString(t.layout()) << "\n"; + + Tensor tensor; + tensor.Resize(t.dims()); + if (platform::is_cpu_place(t.place())) { + tensor.ShareDataWith(t); + } else { + platform::CPUPlace place; + framework::TensorCopy(t, place, &tensor); + platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance(); + auto& dev_ctx = *pool.Get(t.place()); + dev_ctx.Wait(); + } + +#define PrintTensorCallback(cpp_type, proto_type) \ + do { \ + if (tensor.type() == proto_type) { \ + os << "\tdtype: " << proto_type << "\n"; \ + print_tensor(os, tensor); \ + return os; \ + } \ + } while (0) + + _ForEachDataType_(PrintTensorCallback); + VLOG(1) << "PrintVar: unrecognized data type:" << t.type(); + return os; +} + } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/tensor_util.h b/paddle/fluid/framework/tensor_util.h index 1ffd357e62b4bdc72dbec627c463730aa2c8f720..e382f920399ad171d2aeafc30ac8a480fd97e608 100644 --- a/paddle/fluid/framework/tensor_util.h +++ b/paddle/fluid/framework/tensor_util.h @@ -151,5 +151,7 @@ void TensorToVector(const Tensor& src, std::vector* dst) { memory::Copy(dst_place, dst_ptr, boost::get(src.place()), src_ptr, size); } + +std::ostream& operator<<(std::ostream& os, const Tensor& t); } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/trainer.h b/paddle/fluid/framework/trainer.h index b29736cfbbebc183d969dcf1863a6a1d097d2358..b491725974ca117a1ddd7573e46ecc5d127759cf 100644 --- a/paddle/fluid/framework/trainer.h +++ b/paddle/fluid/framework/trainer.h @@ -91,5 +91,58 @@ class DistMultiTrainer : public MultiTrainer { std::shared_ptr pull_dense_worker_; }; +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +class PipelineTrainer : public TrainerBase { + public: + PipelineTrainer() {} + ~PipelineTrainer() override {} + void Initialize(const TrainerDesc& trainer_desc, Dataset* data_set) override; + void InitTrainerEnv(const ProgramDesc& main_program, + const platform::Place& place) override; + void InitOtherEnv(const ProgramDesc& main_program) override {} + void Run() override; + void Finalize() override; + + protected: + int section_num_; + int pipeline_num_; + int scope_queue_size_; + int sync_steps_; + + SectionWorkerParameter pipeline_config_; + + // The in/output var names for each section + std::vector>> in_var_names_; + std::vector>> out_var_names_; + + // Counter for the running thread + std::vector> worker_count_; + std::vector>> worker_count_mutex_; + + // worker: [section_id][pipeline_id][thread_id] + std::vector>>> + workers_; + std::vector section_threads_; + + // We use scope to maintain context info, and scopes + // will be deliverd between different sections. + std::vector>> scope_queues_; + std::vector pipeline_scopes_; + + // The parameters that should be syncronized between different cards using + // nccl all-reduce + std::shared_ptr> param_need_sync_; + std::vector> sync_functors_; + std::shared_ptr nccl_ctx_map_; + + std::vector> readers_; + + void InitFirstScopeQueue(ScopeQueue* scope_queue, int pipeline_id, + const ProgramDesc& main_program); + void CopyParameters(const Scope& root_scope, int pipeline_id); + void construct_sync_functor(); +}; +#endif } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/trainer_desc.proto b/paddle/fluid/framework/trainer_desc.proto index 4fc05ccf5c9be37e80b4ae7263166ad76eb6d6a7..4910fb740c507f9c415b4dfcaee16c89a95ca6f7 100644 --- a/paddle/fluid/framework/trainer_desc.proto +++ b/paddle/fluid/framework/trainer_desc.proto @@ -13,7 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. */ syntax = "proto2"; +option optimize_for = LITE_RUNTIME; import "data_feed.proto"; +import "framework.proto"; package paddle.framework; message TrainerDesc { @@ -30,11 +32,13 @@ message TrainerDesc { repeated string filelist = 5; optional bool debug = 6 [ default = false ]; optional FetchConfig fetch_config = 7; + optional bool use_cvm = 8 [ default = false ]; // device worker parameters optional HogwildWorkerParameter hogwild_param = 101; optional DownpourWorkerParameter downpour_param = 103; optional PullDenseWorkerParameter pull_dense_param = 102; + optional SectionWorkerParameter section_param = 104; // datafeed desc optional DataFeedDesc data_desc = 201; } @@ -50,6 +54,30 @@ message DownpourWorkerParameter { optional bool push_dense = 6 [ default = true ]; } +message SectionWorkerParameter { + repeated SectionConfig section_config = 1; + optional int32 queue_size = 2 [ default = 1 ]; + optional int64 sync_steps = 3 [ default = 1 ]; + optional int32 start_cpu_core_id = 4 [ default = 1 ]; + repeated string param_need_sync = 5; +} + +message SectionConfig { + enum Place { + CPUPlace = 0; + CUDAPlace = 1; + CUDAPinnedPlace = 2; + } + + // FIXME: How to use proto::ProgramDesc + // required string program_desc_str = 1; + optional proto.ProgramDesc program_desc = 1; + optional Place place = 2; + optional int32 concurrency = 3 [ default = 1 ]; + repeated string section_in_var_names = 4; + repeated string section_out_var_names = 5; +} + message FetchConfig { enum Method { PRINT = 0; } repeated string fetch_var_names = 1; diff --git a/paddle/fluid/framework/trainer_factory.cc b/paddle/fluid/framework/trainer_factory.cc index 6b4461c0c429d5b1809dd69d91390421cc8b14ad..ce0eb5ec30c55d757a44a6cc2c374267c52c4adc 100644 --- a/paddle/fluid/framework/trainer_factory.cc +++ b/paddle/fluid/framework/trainer_factory.cc @@ -63,5 +63,8 @@ std::shared_ptr TrainerFactory::CreateTrainer( REGISTER_TRAINER_CLASS(MultiTrainer); REGISTER_TRAINER_CLASS(DistMultiTrainer); +#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) +REGISTER_TRAINER_CLASS(PipelineTrainer); +#endif } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/var_type_traits.cc b/paddle/fluid/framework/var_type_traits.cc index a37b1fbab8cfd0642beaf725c02941002b2176b3..7cc2b3b42258942e6016486f7cf7ecfcae92b91c 100644 --- a/paddle/fluid/framework/var_type_traits.cc +++ b/paddle/fluid/framework/var_type_traits.cc @@ -13,6 +13,7 @@ // limitations under the License. #include "paddle/fluid/framework/var_type_traits.h" +#include #include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/reader.h" #include "paddle/fluid/framework/scope.h" @@ -22,6 +23,7 @@ #ifdef PADDLE_WITH_CUDA #ifndef _WIN32 #include "paddle/fluid/operators/nccl/nccl_gpu_common.h" +#include "paddle/fluid/platform/nccl_helper.h" #endif #include #include "paddle/fluid/operators/conv_cudnn_op_cache.h" diff --git a/paddle/fluid/framework/var_type_traits.h b/paddle/fluid/framework/var_type_traits.h index fa77b96a7bdfa28ed982db022e8e5ecaef0b443c..7147f06233cb9d435d8be62814df0a3891b729fb 100644 --- a/paddle/fluid/framework/var_type_traits.h +++ b/paddle/fluid/framework/var_type_traits.h @@ -36,6 +36,7 @@ namespace platform { #ifdef PADDLE_WITH_CUDA #ifndef _WIN32 class Communicator; +class NCCLCommunicator; #endif #endif } // namespace platform @@ -140,7 +141,7 @@ using VarTypeRegistry = detail::VarTypeRegistryImpl< std::map, operators::reader::LoDTensorBlockingQueueHolder, #ifdef PADDLE_WITH_CUDA #ifndef _WIN32 - ncclUniqueId, platform::Communicator, + ncclUniqueId, platform::Communicator, platform::NCCLCommunicator, #endif operators::CudnnRNNCache, #endif diff --git a/paddle/fluid/framework/var_type_traits_test.cc b/paddle/fluid/framework/var_type_traits_test.cc index a47275e1ca25a4f66e67b4986ec78e49ea952a51..67dbfd740ed9b71fa06b684c14720ae2814fe11c 100644 --- a/paddle/fluid/framework/var_type_traits_test.cc +++ b/paddle/fluid/framework/var_type_traits_test.cc @@ -26,6 +26,7 @@ #ifdef PADDLE_WITH_CUDA #ifndef _WIN32 #include "paddle/fluid/operators/nccl/nccl_gpu_common.h" +#include "paddle/fluid/platform/nccl_helper.h" #endif #include "paddle/fluid/operators/conv_cudnn_op_cache.h" #include "paddle/fluid/operators/cudnn_rnn_cache.h" diff --git a/paddle/fluid/imperative/CMakeLists.txt b/paddle/fluid/imperative/CMakeLists.txt index e52a0283f726640eb56b24a2978af6ee44e658ff..73c629fd227aee0bf90c4049a2f66f717e939984 100644 --- a/paddle/fluid/imperative/CMakeLists.txt +++ b/paddle/fluid/imperative/CMakeLists.txt @@ -1,9 +1,10 @@ +cc_library(imperative_flag SRCS flags.cc DEPS gflags) + if(WITH_PYTHON) -cc_library(layer SRCS layer.cc DEPS proto_desc operator device_context blas pybind) -cc_library(tracer SRCS tracer.cc DEPS proto_desc device_context pybind) +cc_library(layer SRCS layer.cc DEPS proto_desc operator device_context blas pybind profiler imperative_flag) +cc_library(tracer SRCS tracer.cc DEPS proto_desc device_context pybind profiler) cc_library(engine SRCS engine.cc) cc_library(imperative_profiler SRCS profiler.cc) cc_library(nccl_context SRCS nccl_context.cc DEPS device_context) - cc_test(nccl_context_test SRCS nccl_context_test.cc DEPS nccl_context) endif() diff --git a/paddle/fluid/imperative/layer.cc b/paddle/fluid/imperative/layer.cc index aa739a8972ec1bf6806fe0d5a3e5e4fd1d6f807d..fb22d3349028f6a5ecb2dcbae8e8d08c6806ca1c 100644 --- a/paddle/fluid/imperative/layer.cc +++ b/paddle/fluid/imperative/layer.cc @@ -14,6 +14,7 @@ #include "paddle/fluid/imperative/layer.h" +#include #include #include #include @@ -27,15 +28,32 @@ #include "paddle/fluid/framework/tensor_util.h" #include "paddle/fluid/operators/math/blas.h" #include "paddle/fluid/platform/device_context.h" +#include "paddle/fluid/platform/profiler.h" #include "paddle/fluid/string/printf.h" namespace paddle { namespace imperative { -const char* PyLayer::kFwdInp = "X"; -const char* PyLayer::kFwdOut = "Out"; +void ThreadSafeNameSet::Insert(const std::string& name) { + std::lock_guard guard(mtx_); + set_.insert(name); +} + +void ThreadSafeNameSet::Remove(const std::string& name) { + std::lock_guard guard(mtx_); + auto iter = set_.find(name); + PADDLE_ENFORCE(iter != set_.end(), "%s does not exist", name); + set_.erase(iter); +} + +std::vector ThreadSafeNameSet::Names() const { + std::lock_guard guard(mtx_); + return std::vector(set_.begin(), set_.end()); +} + +ThreadSafeNameSet VarBase::name_set_; -std::map py_funcs_; +std::vector VarBase::AliveVarNames() { return name_set_.Names(); } using framework::Variable; @@ -81,62 +99,106 @@ class TensorAddToFunctor : public boost::static_visitor<> { } // namespace detail -void AddTo(Variable* src, Variable* dst, platform::Place place) { - framework::Tensor* dst_tensor = dst->GetMutable(); - framework::Tensor* src_tensor = src->GetMutable(); - - // FIXME(minqiyang): loss_grad op will pass a zero grad of label - // ugly fix for it - if (src_tensor->numel() == 0) { +void AddTo(std::shared_ptr src, std::shared_ptr dst, + platform::Place place, GradientRef* grad_ref) { + PADDLE_ENFORCE(grad_ref->find(dst.get()) != grad_ref->end(), + "gradient %s are not found in grad_ref", dst->Name()); + if ((*grad_ref)[dst.get()].second) { + PADDLE_ENFORCE(src->IsInitialize(), "Using uninitialized VarBase"); + dst->var_ = std::move(src->var_); + (*grad_ref)[dst.get()].second = false; + if (!dst->IsInitialize()) { + dst->SetInitialize(true); + } return; + } else { + framework::Tensor* dst_tensor = + dst->var_->GetMutable(); + framework::Tensor* src_tensor = + src->var_->GetMutable(); + + // FIXME(minqiyang): loss_grad op will pass a zero grad of label + // ugly fix for it + if (src_tensor->numel() == 0) { + return; + } + + PADDLE_ENFORCE(dst_tensor->numel() == src_tensor->numel(), + "dst_numel %lld vs. src_numel %lld", dst_tensor->numel(), + src_tensor->numel()); + + detail::TensorAddToFunctor func( + src_tensor->numel(), src_tensor->data(), + dst_tensor->mutable_data(place)); + boost::apply_visitor(func, place); } +} - PADDLE_ENFORCE(dst_tensor->numel() == src_tensor->numel(), - "dst_numel %lld vs. src_numel %lld", dst_tensor->numel(), - src_tensor->numel()); +void ZeroGrads(const std::shared_ptr vb, + const platform::Place& place) { + platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance(); + auto* dev_ctx = pool.Get(place); + auto grad_t = vb->var_->GetMutable(); + operators::math::set_constant(*dev_ctx, grad_t, 0.0); +} - detail::TensorAddToFunctor func( - src_tensor->numel(), src_tensor->data(), - dst_tensor->mutable_data(place)); - boost::apply_visitor(func, place); +void AddGradBySort(BackwardSumMap* bck_map, + std::shared_ptr target, + GradientRef* grad_ref) { + PADDLE_ENFORCE(bck_map->find(target.get()) != bck_map->end(), + "Can't find %s in backward grad map", target->Name()); + std::pair>>>& + current = bck_map->at(target.get()); + std::sort(current.second.begin(), current.second.end(), + [](const std::pair>& a, + const std::pair>& b) { + return a.first > b.first; + }); + for (auto& var_pair : current.second) { + VLOG(10) << "add origin_grad: " << target->Name(); + VLOG(10) << "added grad: " << var_pair.second->Name() + << " trace id is: " << var_pair.first; + AddTo(var_pair.second, target, current.first, grad_ref); + var_pair.second.reset(); + } } class Autograd { public: Autograd() {} - void RunBackward(VarBase* var) { + void RunBackward(VarBase* var, const detail::BackwardStrategy& bck_stratedy) { if (var->IsStopGradient()) { return; } - VLOG(3) << "start autograd"; - + VLOG(2) << "start autograd"; + BackwardSumMap bck_map; std::deque ready; ready.push_back(var->PreOp()); - std::map dep_counts = ComputeDepCounts(var->PreOp()); + std::map dep_counts = + ComputeDepCounts(var->PreOp(), bck_stratedy, &grad_ref); while (!ready.empty()) { OpBase* ready_op = ready.front(); ready.pop_front(); - std::map> input_grads = - ready_op->ApplyGrad(); - - for (auto it = input_grads.rbegin(); it != input_grads.rend(); ++it) { - const std::vector& ingrads = it->second; - for (size_t i = 0; i < ingrads.size(); ++i) { - if (!ingrads[i]) continue; - if (ready_op->input_vars_[it->first][i]->IsStopGradient()) { - continue; - } - OpBase* pre_op = ready_op->pre_ops_[it->first][i]; - if (!pre_op) continue; - - dep_counts[pre_op] -= 1; - PADDLE_ENFORCE(dep_counts[pre_op] >= 0); - bool pre_op_ready = dep_counts[pre_op] == 0; - if (pre_op_ready) { - ready.push_back(pre_op); + std::vector grads_outputs = + ready_op->ApplyGrad(&bck_map, &grad_ref, bck_stratedy); + + for (const auto& map : grads_outputs) { + for (auto it = map.rbegin(); it != map.rend(); ++it) { + const std::vector>& grad_outs = it->second; + for (size_t i = 0; i < grad_outs.size(); ++i) { + if (!grad_outs[i] || grad_outs[i]->IsStopGradient()) continue; + OpBase* pre_op = grad_outs[i]->PreOp(); + if (!pre_op) continue; + dep_counts[pre_op] -= 1; + PADDLE_ENFORCE(dep_counts[pre_op] >= 0); + bool pre_op_ready = dep_counts[pre_op] == 0; + if (pre_op_ready) { + ready.push_back(pre_op); + } } } } @@ -146,7 +208,14 @@ class Autograd { } private: - std::map ComputeDepCounts(OpBase* op) { + std::map ComputeDepCounts( + OpBase* op, const detail::BackwardStrategy& bck_stratedy, + GradientRef* grad_ref) { + if (bck_stratedy.sorted_sum_gradient_) { + PADDLE_ENFORCE_NOT_NULL(grad_ref, + "grad_ref should not be null when " + "using sorted grad backward strategy"); + } std::map ret; std::deque queue; @@ -156,10 +225,21 @@ class Autograd { while (!queue.empty()) { OpBase* candidate = queue.front(); queue.pop_front(); + for (const auto& map : candidate->grad_output_vars_) { + for (const auto& it : map) { + for (const auto& vb : it.second) { + if (bck_stratedy.sorted_sum_gradient_) { + ++(*grad_ref)[vb.get()].first; + } + // init the state of the grad_ + (*grad_ref)[vb.get()].second = true; + } + } + } for (auto it : candidate->pre_ops_) { for (OpBase* pre_op : it.second) { if (!pre_op) continue; - VLOG(5) << "op dep " << candidate->Type() << " trace id " + VLOG(2) << "op dep " << candidate->Type() << " trace id " << candidate->trace_id_ << " <---- " << it.first << " <---- " << pre_op->Type() << " trace id " << pre_op->trace_id_; if (visited.find(pre_op) == visited.end()) { @@ -172,6 +252,8 @@ class Autograd { } return ret; } + + GradientRef grad_ref; }; std::unique_ptr VarBase::NewVarBase(const platform::Place& dst_place, @@ -187,16 +269,14 @@ std::unique_ptr VarBase::NewVarBase(const platform::Place& dst_place, new_var->var_->GetMutable(); tensor->set_lod(var_->Get().lod()); + const auto& src_tensor = var_->Get(); + framework::TensorCopy(src_tensor, dst_place, tensor); if (blocking) { - platform::DeviceContext* dev_ctx = - platform::DeviceContextPool::Instance().Get(dst_place); - - framework::TensorCopySync(var_->Get(), dst_place, - tensor); - - dev_ctx->Wait(); - } else { - framework::TensorCopy(var_->Get(), dst_place, tensor); + platform::DeviceContextPool::Instance().Get(dst_place)->Wait(); + auto src_place = src_tensor.place(); + if (!(src_place == dst_place)) { + platform::DeviceContextPool::Instance().Get(src_place)->Wait(); + } } if (platform::is_gpu_place(dst_place)) { @@ -213,98 +293,94 @@ framework::LoDTensor& VarBase::GradValue() { return *(grads_->var_->GetMutable()); } -std::map> OpBase::ApplyGrad() { - PADDLE_ENFORCE(!grad_op_descs_.empty() || backward_id_ > 0, - "%s has no backward implementation", Type()); - +std::vector OpBase::ApplyGrad( + BackwardSumMap* bck_map, GradientRef* grad_ref, + const detail::BackwardStrategy& bck_stratedy) { + PADDLE_ENFORCE(!grad_op_descs_.empty(), "%s has no backward implementation", + Type()); VLOG(3) << "apply op grad: " << Type(); std::vector tmp_grad_outputs; - if (backward_id_ > 0) { - VLOG(3) << "py_layer_grad"; - tmp_grad_outputs.resize(1); - tmp_grad_outputs[0][framework::GradVarName(PyLayer::kFwdOut)] = - PyLayer::ApplyGrad( - backward_id_, - grad_input_vars_[0][framework::GradVarName(PyLayer::kFwdInp)]); - } else { - const size_t grad_op_count = grad_op_descs_.size(); - - tmp_grad_outputs.resize(grad_op_count); - for (size_t k = 0; k < grad_op_count; ++k) { - framework::OpDesc* grad_op_desc = grad_op_descs_[k]; - auto& grad_output_variable_map = grad_output_vars_[k]; - - VLOG(3) << "apply grad op " << grad_op_desc->Type(); - - // Allocate tmp grad output variable - for (const auto& it : grad_output_variable_map) { - auto& outputs = tmp_grad_outputs[k][it.first]; - outputs.reserve(it.second.size()); - for (size_t i = 0; i < it.second.size(); ++i) { - VarBase* origin_grad_var_base = it.second[i]; - - // Allocate a new variable - VarBase* tmp_grad_var_base = new VarBase( - string::Sprintf("%s@IGrad", origin_grad_var_base->Name()), - origin_grad_var_base->DataType(), origin_grad_var_base->Dims(), - place_, true, false); - outputs.emplace_back(tmp_grad_var_base); - } - } + const size_t grad_op_count = grad_op_descs_.size(); - // No need to do compile time infer shape here. - // grad_op_desc_->InferShape(*block_); - // grad_op_desc->InferVarType(block_); + tmp_grad_outputs.resize(grad_op_count); + for (size_t k = 0; k < grad_op_count; ++k) { + framework::OpDesc* grad_op_desc = grad_op_descs_[k]; + platform::RecordEvent record_event(grad_op_desc->Type()); + auto& grad_output_variable_map = grad_output_vars_[k]; + VLOG(3) << "apply grad op " << grad_op_desc->Type(); - std::unique_ptr opbase = - framework::OpRegistry::CreateOp(*grad_op_desc); - - auto& info = framework::OpInfoMap::Instance().Get(grad_op_desc->Type()); - if (info.infer_var_type_) { - RuntimeInferVarTypeContext infer_var_type_ctx( - &grad_input_vars_[k], &tmp_grad_outputs[k], &attrs_); - info.infer_var_type_(&infer_var_type_ctx); + // Allocate tmp grad output variable + for (const auto& it : grad_output_variable_map) { + auto& outputs = tmp_grad_outputs[k][it.first]; + outputs.reserve(it.second.size()); + for (const std::shared_ptr& origin_grad_var_base : + it.second) { + // Allocate a new variable + std::shared_ptr tmp_grad_var_base(new VarBase( + string::Sprintf("%s@IGrad", origin_grad_var_base->Name()), + origin_grad_var_base->DataType(), origin_grad_var_base->Dims(), + place_, true, false)); + outputs.emplace_back(std::move(tmp_grad_var_base)); } + } - framework::OperatorWithKernel* op_kernel = - dynamic_cast(opbase.get()); - PADDLE_ENFORCE_NOT_NULL(op_kernel, "only support op with kernel"); + // No need to do compile time infer shape here. + // grad_op_desc_->InferShape(*block_); + // grad_op_desc->InferVarType(block_); - // Run grad op - framework::VariableValueMap grad_invars_map; - framework::VariableValueMap grad_outvars_map; + std::unique_ptr opbase = + framework::OpRegistry::CreateOp(*grad_op_desc); - for (const auto& it : grad_input_vars_[k]) { - auto& grad_invars = grad_invars_map[it.first]; - grad_invars.reserve(it.second.size()); - for (const VarBase* grad_inp : it.second) { - PADDLE_ENFORCE_NOT_NULL(grad_inp->var_, "op %s input %s nullptr", - grad_op_desc->Type(), grad_inp->Name()); + auto& info = framework::OpInfoMap::Instance().Get(grad_op_desc->Type()); + if (info.infer_var_type_) { + RuntimeInferVarTypeContext infer_var_type_ctx( + &grad_input_vars_[k], &tmp_grad_outputs[k], &(opbase->Attrs())); + info.infer_var_type_(&infer_var_type_ctx); + } - grad_invars.emplace_back(grad_inp->var_); + framework::OperatorWithKernel* op_kernel = + dynamic_cast(opbase.get()); + PADDLE_ENFORCE_NOT_NULL(op_kernel, "only support op with kernel"); + + // Run grad op + framework::VariableValueMap grad_invars_map; + framework::VariableValueMap grad_outvars_map; + + for (const auto& it : grad_input_vars_[k]) { + auto& grad_invars = grad_invars_map[it.first]; + grad_invars.reserve(it.second.size()); + for (const std::shared_ptr& grad_inp : it.second) { + PADDLE_ENFORCE_NOT_NULL(grad_inp->var_, "op %s input %s nullptr", + grad_op_desc->Type(), grad_inp->Name()); + if (!grad_inp->IsInitialize()) { + grad_inp->InitBuffer(); + ZeroGrads(grad_inp, place_); } + const std::shared_ptr& const_grad_inp = grad_inp; + grad_invars.emplace_back(const_grad_inp->var_.get()); } + } - for (const auto& it : tmp_grad_outputs[k]) { - auto& grad_outvars = grad_outvars_map[it.first]; - grad_outvars.reserve(it.second.size()); - for (VarBase* grad_out : it.second) { - PADDLE_ENFORCE_NOT_NULL(grad_out->var_, "op %s output %s nullptr", - grad_op_desc->Type(), grad_out->Name()); + for (const auto& it : tmp_grad_outputs[k]) { + auto& grad_outvars = grad_outvars_map[it.first]; + grad_outvars.reserve(it.second.size()); + for (const std::shared_ptr& grad_out : it.second) { + PADDLE_ENFORCE_NOT_NULL(grad_out->var_, "op %s output %s nullptr", + grad_op_desc->Type(), grad_out->Name()); - grad_outvars.emplace_back(grad_out->var_); - } + grad_outvars.emplace_back(grad_out->var_.get()); } - - framework::RuntimeContext ctx(grad_invars_map, grad_outvars_map); - framework::Scope scope; - PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place_); - p.op.RuntimeInferShape(scope, place_, ctx); - p.func( - framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx, nullptr)); } + + framework::RuntimeContext ctx(grad_invars_map, grad_outvars_map); + framework::Scope scope; + PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place_); + p.op.RuntimeInferShape(scope, place_, ctx); + p.func( + framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx, nullptr)); } + platform::RecordEvent record_event("merge_grads"); // Add tmp grad outputs to original grad vars for (size_t k = 0; k < grad_output_vars_.size(); ++k) { for (const auto& it : grad_output_vars_[k]) { @@ -313,18 +389,50 @@ std::map> OpBase::ApplyGrad() { PADDLE_ENFORCE_EQ(outputs.size(), origin_outputs.size()); for (size_t i = 0; i < outputs.size(); ++i) { - framework::Variable* grad = outputs[i]->var_; - framework::Variable* orig_grad = origin_outputs[i]->var_; - VLOG(3) << "AddTo Called with orig_grad is: " - << origin_outputs[i]->name_ << " Grad to be added is " - << outputs[i]->name_; - AddTo(grad, orig_grad, place_); - delete grad; + // track outputs used by sum + if (bck_stratedy.sorted_sum_gradient_) { + if (bck_map->find(origin_outputs[i].get()) != bck_map->end()) { + VLOG(10) << "add sub grad to " << origin_outputs[i]->Name(); + bck_map->at(origin_outputs[i].get()) + .second.emplace_back( + std::pair>( + this->trace_id_, std::move(outputs[i]))); + } else { + VLOG(10) << "insert new map for " << origin_outputs[i]->Name(); + std::pair>>> + tmp(place_, + {std::make_pair(this->trace_id_, std::move(outputs[i]))}); + bck_map->insert(std::make_pair(origin_outputs[i].get(), tmp)); + } + + PADDLE_ENFORCE( + grad_ref->find(origin_outputs[i].get()) != grad_ref->end(), + "Can't find %s in grad_reference count map", + origin_outputs[i]->Name()); + PADDLE_ENFORCE(grad_ref->at(origin_outputs[i].get()).first >= 1, + "Backward error when calculate grad reference"); + if (grad_ref->at(origin_outputs[i].get()).first > 1) { + VLOG(10) << "remove ref for " << origin_outputs[i]->Name(); + grad_ref->at(origin_outputs[i].get()).first--; + } else { + VLOG(10) << "Add grad for: " << origin_outputs[i]->Name(); + AddGradBySort(bck_map, origin_outputs[i], grad_ref); + grad_ref->at(origin_outputs[i].get()).first--; + } + } else { + VLOG(10) << "AddTo Called with orig_grad is: " + << origin_outputs[i]->name_ << " Grad to be added is " + << outputs[i]->name_; + AddTo(outputs[i], origin_outputs[i], place_, grad_ref); + outputs[i].reset(); + } } } } - return input_vars_; + return grad_output_vars_; } void OpBase::InvokeBackwardHooks() { @@ -336,94 +444,25 @@ void OpBase::InvokeBackwardHooks() { } } -void OpBase::RegisterBackwardHooks(const py::object& callable, bool front) { +void OpBase::RegisterBackwardHooks(const py::object& callable) { VLOG(3) << "Register backward hooks " << trace_id_; // TODO(minqiyang): check the callable format - if (front) { - backward_hooks_.insert(backward_hooks_.begin(), callable); - } else { - backward_hooks_.push_back(callable); - } + backward_hooks_.push_back(callable); } -void VarBase::RunBackward() { +void VarBase::RunBackward(const detail::BackwardStrategy& bck_stratedy) { if (!pre_op_) return; - + platform::RecordEvent record_event("Imperative Backward"); VLOG(3) << "start backward"; + grads_->InitBuffer(); auto grads_t = grads_->var_->GetMutable(); operators::math::set_constant( *(platform::DeviceContextPool::Instance().Get( var_->GetMutable()->place())), grads_t, 1.0); - PADDLE_ENFORCE( - grads_ == - pre_op_->output_vars_[pre_op_out_name_][pre_op_out_idx_]->grads_); - Autograd().RunBackward(this); -} - -void PyLayer::RegisterFunc(int func_id, const py::object& py_func) { - py_funcs_[func_id] = py_func; -} - -int PyLayer::NumFuncs() { return py_funcs_.size(); } - -std::vector PyLayer::Apply( - int func_id, const std::vector& inputs) { - PADDLE_ENFORCE(py_funcs_.find(func_id) != py_funcs_.end()); - return CallPythonFunc(py_funcs_[func_id], inputs); -} - -std::vector PyLayer::ApplyGrad(int func_id, - const std::vector& inputs) { - PADDLE_ENFORCE(py_funcs_.find(func_id) != py_funcs_.end()); - auto rets = CallPythonFunc(py_funcs_[func_id], inputs); - - std::vector outs; - outs.reserve(rets.size()); - for (size_t i = 0U; i != rets.size(); ++i) { - outs.emplace_back(new VarBase( - string::Sprintf("%s_out_%d", framework::GradVarName(PyLayer::kFwdOut), - i), - rets[i], nullptr, true)); - } - - return outs; -} - -std::vector PyLayer::CallPythonFunc( - const py::object& callable, const std::vector& ins) { - py::gil_scoped_acquire guard; - py::tuple in_args(ins.size()); - for (size_t i = 0; i < ins.size(); ++i) { - const framework::LoDTensor& t = ins[i]->var_->Get(); - in_args[i] = t.IsInitialized() ? py::cast(t) : py::cast(nullptr); - } - VLOG(3) << "pyfunc in " << py::len(in_args); - - // TODO(panyx0718): Who owns the returned LoDTensor. - auto ret = callable(in_args); - auto ret_tuple = py::cast(ret); - size_t ret_num = py::len(ret_tuple); - std::vector outs; - outs.reserve(ret_num); - VLOG(3) << "pyfunc out " << ret_num; - for (size_t i = 0; i < ret_num; ++i) { - try { - auto* py_out_tensor = py::cast(ret_tuple[i]); - PADDLE_ENFORCE_NOT_NULL(py_out_tensor, - "Output tensor %d should not be nullptr", i); - auto* var = new framework::Variable(); - auto* tensor = var->GetMutable(); - tensor->ShareDataWith(*py_out_tensor); - tensor->set_lod(py_out_tensor->lod()); - outs.emplace_back(var); - } catch (py::cast_error&) { - PADDLE_THROW("The %d-th output must be LoDTensor", i); - } - } - return outs; + Autograd().RunBackward(this, bck_stratedy); } } // namespace imperative diff --git a/paddle/fluid/imperative/layer.h b/paddle/fluid/imperative/layer.h index 37488d381ef2fe15f96a5b55434eca40466a1424..2fbedd82ea59a89fed20639ba4873889289a5a3b 100644 --- a/paddle/fluid/imperative/layer.h +++ b/paddle/fluid/imperative/layer.h @@ -14,16 +14,20 @@ #pragma once +#include +#include // NOLINT +#include // NOLINT +#include // NOLINT +#include +#include // NOLINT +#include // NOLINT +#include +#include // NOLINT + // clang-format off #include "paddle/fluid/framework/python_headers.h" // clang-format on -#include // NOLINT -#include // NOLINT -#include // NOLINT -#include // NOLINT -#include // NOLINT - #include "paddle/fluid/framework/op_desc.h" #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/framework/var_desc.h" @@ -31,8 +35,9 @@ #include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/device_context.h" #include "paddle/fluid/operators/math/math_function.h" - +#include "paddle/fluid/imperative/backward_strategy.h" #include "paddle/fluid/imperative/type_defs.h" +#include "paddle/fluid/imperative/flags.h" namespace paddle { namespace imperative { @@ -107,6 +112,19 @@ class PreparedOp { class OpBase; +class ThreadSafeNameSet { + public: + void Insert(const std::string& name); + + void Remove(const std::string& name); + + std::vector Names() const; + + private: + std::multiset set_; + mutable std::mutex mtx_; +}; + /* The wrapper for Variable which holds a Variable and a VarBase of its * gradient. This object should be managed totally by Python intepreter. * @@ -114,13 +132,17 @@ class OpBase; */ class VarBase { public: + static std::vector AliveVarNames(); + // Internal interface, create VarBase from exist variable - VarBase(const std::string& name, framework::Variable* var, VarBase* grad, - bool stop_gradient) + VarBase(const std::string& name, std::unique_ptr var, + VarBase* grad, bool stop_gradient) : VarBase(name, var->Get().type(), var->Get().dims(), - var->Get().place(), var, grad, - stop_gradient, false) {} + var->Get().place(), nullptr, grad, + stop_gradient, false, true) { + var_ = std::move(var); + } // Python interface VarBase(const std::string& name, const framework::proto::VarType::Type dtype, @@ -134,52 +156,69 @@ class VarBase { const framework::DDim& shape, const platform::Place& place, bool stop_gradient, bool persistable) : VarBase(name, dtype, shape, place, nullptr, nullptr, stop_gradient, - persistable) {} + persistable, true) {} + + // Grad used constructor + VarBase(const std::string& name, const framework::proto::VarType::Type dtype, + const std::vector& shape, const platform::Place& place, + bool stop_gradient, bool persistable, bool need_initialize) + : VarBase(name, dtype, framework::make_ddim(shape), place, nullptr, + nullptr, stop_gradient, persistable, need_initialize) {} private: // TODO(minqiyang): need support SelectedRows VarBase(const std::string& name, framework::proto::VarType::Type dtype, const framework::DDim& shape, const platform::Place& place, - framework::Variable* var, VarBase* grad, bool stop_gradient, - bool persistable) + std::unique_ptr var, VarBase* grad, + bool stop_gradient, bool persistable, bool need_initialize) : name_(name), type_(framework::proto::VarType::LOD_TENSOR), - var_(var), + place_(place), + var_(std::move(var)), grads_(grad), + dtype_(dtype), stop_gradient_(stop_gradient), persistable_(persistable), pre_op_(nullptr), pre_op_out_name_(), pre_op_out_idx_(-1) { if (!var_) { - var_ = new framework::Variable(); + var_.reset(new framework::Variable()); } + auto tensor = var_->GetMutable(); tensor->Resize(shape); - tensor->mutable_data(place, dtype); - VLOG(10) << "create varbase: " << name_ << " type: " << dtype - << " place: " << place; - } - - public: - virtual ~VarBase() { - if (var_) { - delete var_; - var_ = nullptr; + if (need_initialize) { + tensor->mutable_data(place, dtype); + is_initialized_ = true; + VLOG(8) << "initialized varbase: " << name_ << " type: " << dtype + << " place: " << place; + } else { + is_initialized_ = false; + VLOG(8) << "not initialized varbase: " << name_; } + VLOG(8) << "create varbase: " << name_ << " type: " << dtype + << " place: " << place << "Stop gradient: " << stop_gradient_; - if (grads_) { - delete grads_; - grads_ = nullptr; + if (IsDebugEnabled()) { + name_set_.Insert(name_); } + } + public: + virtual ~VarBase() { pre_op_ = nullptr; pre_op_out_idx_ = -1; + VLOG(8) << "destruct varbase: " << name_; + if (IsDebugEnabled()) { + name_set_.Remove(name_); + } } inline void SetName(const std::string& name) { name_ = name; } inline std::string Name() const { return name_; } - + inline bool IsInitialize() const { return is_initialized_; } + inline void SetInitialize(bool inited) { is_initialized_ = inited; } inline std::vector Shape() const { if (var_->IsInitialized()) { return framework::vectorize(var_->Get().dims()); @@ -197,10 +236,7 @@ class VarBase { auto tensor = var_->GetMutable(); tensor->mutable_data(tensor->place(), type); } - inline framework::proto::VarType::Type DataType() const { - auto tensor = var_->Get(); - return tensor.type(); - } + inline framework::proto::VarType::Type DataType() const { return dtype_; } // tensor type. e.g.. LoDTensor inline void SetType(framework::proto::VarType::Type type) { type_ = type; } @@ -208,16 +244,20 @@ class VarBase { inline void SetStopGradient(bool stop_gradient) { stop_gradient_ = stop_gradient; + if (grads_) { + grads_->stop_gradient_ = stop_gradient; + } } inline bool IsStopGradient() const { return stop_gradient_; } inline void SetPersistable(bool persistable) { persistable_ = persistable; } inline bool IsPersistable() const { return persistable_; } - + inline void SetPreOp(OpBase* op) { pre_op_ = op; } + inline platform::Place GetPlace() { return place_; } inline OpBase* PreOp() const { return pre_op_; } inline int PreOpOutIdx() const { return pre_op_out_idx_; } - void RunBackward(); + void RunBackward(const detail::BackwardStrategy& bck_stratedy); inline void ResetPreOp(OpBase* op) { if (op == pre_op_) { @@ -227,6 +267,17 @@ class VarBase { } } + void InitBuffer() { + if (!is_initialized_) { + var_->GetMutable()->mutable_data(place_, dtype_); + is_initialized_ = true; + VLOG(8) << "initialized varbase: " << name_ << " type: " << dtype_ + << " place: " << place_; + } else { + VLOG(8) << "var: " << name_ << " has already been initialized "; + } + } + void TrackPreOp(OpBase* pre_op, const std::string& pre_op_out_name, int pre_op_out_idx, bool pre_op_stop_gradient) { pre_op_ = pre_op; @@ -261,16 +312,20 @@ class VarBase { framework::proto::VarType::Type type_; platform::Place place_; - framework::Variable* var_; - VarBase* grads_; + std::unique_ptr var_; + std::shared_ptr grads_; private: + framework::proto::VarType::Type dtype_; bool stop_gradient_; bool persistable_; - + bool is_initialized_; OpBase* pre_op_; std::string pre_op_out_name_; int pre_op_out_idx_; + + // A private flag to check memory leak + static ThreadSafeNameSet name_set_; }; /* The wrapper for OpDesc which holds a OpDesc and a OpDesc of its @@ -281,28 +336,27 @@ class PYBIND11_HIDDEN OpBase { OpBase(const std::string& type) : type_(type), trace_id_(-1), - forward_id_(-1), - backward_id_(-1), place_(platform::CPUPlace()), backward_hooks_() {} virtual ~OpBase() { - // TODO(minqiyang): remove op_desc from block_desc in tracer - // - // reset all output vars' pre op - for (auto iter : output_vars_) { - for (VarBase* var : iter.second) { - var->ResetPreOp(this); + for (const auto& it : outputs_ref) { + auto vb = it.lock(); + if (vb) { + VLOG(3) << "Op reset by" << vb->name_; + vb->ResetPreOp(this); } } - + // TODO(minqiyang): remove op_desc from block_desc in tracer // release resource for (framework::OpDesc* desc : grad_op_descs_) { delete desc; } } - std::map> ApplyGrad(); + std::vector ApplyGrad( + BackwardSumMap* bck_map, GradientRef* grad_ref, + const detail::BackwardStrategy& bck_stratedy); inline std::string Type() const { return type_; } inline std::string GradOpType(size_t index) const { @@ -310,16 +364,17 @@ class PYBIND11_HIDDEN OpBase { return grad_op_descs_[index]->Type(); } - void RegisterBackwardHooks(const py::object& callable, bool front = false); + void RegisterBackwardHooks(const py::object& callable); void InvokeBackwardHooks(); - void TrackPreOp(const std::string& inp_name, - const std::vector& inputs) { + void TrackPreOp( + const std::string& inp_name, + const std::vector>& inputs) { auto& pre_ops_list = pre_ops_[inp_name]; pre_ops_list.reserve(inputs.size()); auto& pre_ops_out_idx_list = pre_ops_out_idx_[inp_name]; - for (VarBase* inp_var : inputs) { + for (std::shared_ptr inp_var : inputs) { if (inp_var->PreOp() && !inp_var->IsStopGradient()) { VLOG(3) << "add pre op " << inp_var->PreOp()->Type() << " in slot " << inp_name; @@ -335,24 +390,17 @@ class PYBIND11_HIDDEN OpBase { } std::string type_; - // One of `trace_id_` or `forward_id_` is set, not both. - // For pure python PyLayer, use `forward_id_`, otherwise, use trace_id_. int trace_id_; - int forward_id_; - // When has backward, one of `grad_op_descs_` or `backward_id_` is set, - // not both. // Note: each fwd op corresponds to a vector of bwd ops. std::vector grad_op_descs_; - int backward_id_; platform::Place place_; - VarBasePtrMap input_vars_; - VarBasePtrMap output_vars_; OpBasePtrMap pre_ops_; std::map> pre_ops_out_idx_; + VarBaseWeakPtrList outputs_ref; // Inputs to a vector of bwd ops. std::vector grad_input_vars_; // Outputs to a vector of bwd ops. @@ -367,34 +415,13 @@ class Layer { public: virtual ~Layer() {} - virtual std::vector Forward(const std::vector& inputs) { - std::vector vars; + virtual std::vector> Forward( + const std::vector>& inputs) { + std::vector> vars; return vars; } }; -class PyLayer { - public: - virtual ~PyLayer() {} - - static const char* kFwdInp; - static const char* kFwdOut; - - static void RegisterFunc(int func_id, const py::object& py_func); - - static int NumFuncs(); - - static std::vector Apply( - int func_id, const std::vector& inputs); - - static std::vector ApplyGrad(int func_id, - const std::vector& inputs); - - private: - static std::vector CallPythonFunc( - const py::object& callable, const std::vector& ins); -}; - // infer var type context for imperative mode class PYBIND11_HIDDEN RuntimeInferVarTypeContext : public framework::InferVarTypeContext { @@ -411,7 +438,7 @@ class PYBIND11_HIDDEN RuntimeInferVarTypeContext var_set_() { input_names_.reserve(inputs_->size()); for (auto& it : *inputs_) { - for (imperative::VarBase* var : it.second) { + for (std::shared_ptr var : it.second) { input_names_[it.first].emplace_back(var->Name()); var_set_[var->Name()] = var; } @@ -419,7 +446,7 @@ class PYBIND11_HIDDEN RuntimeInferVarTypeContext output_names_.reserve(outputs_->size()); for (auto& it : *outputs_) { - for (imperative::VarBase* var : it.second) { + for (std::shared_ptr var : it.second) { output_names_[it.first].emplace_back(var->Name()); var_set_[var->Name()] = var; } @@ -515,7 +542,8 @@ class PYBIND11_HIDDEN RuntimeInferVarTypeContext const framework::AttributeMap* attrs_; std::unordered_map> input_names_; std::unordered_map> output_names_; - std::unordered_map var_set_; + std::unordered_map> + var_set_; }; } // namespace imperative diff --git a/paddle/fluid/imperative/nccl_context.cc b/paddle/fluid/imperative/nccl_context.cc index f96c83936df590e5bd3abe89b7e7c2a6ddf92d01..d9630bd66d5b85db3a95e865b12f5c2e5c154fcf 100644 --- a/paddle/fluid/imperative/nccl_context.cc +++ b/paddle/fluid/imperative/nccl_context.cc @@ -93,6 +93,7 @@ void NCCLParallelContext::SendNCCLID(const std::string &ep, send(sock, buffer, NCCL_UNIQUE_ID_BYTES, 0); break; } + close(sock); } void NCCLParallelContext::BcastNCCLId(ncclUniqueId *nccl_id, int root) { diff --git a/paddle/fluid/imperative/tracer.cc b/paddle/fluid/imperative/tracer.cc index 7c495ddd68221acfed8537fd72e9a582e891f8db..682bea7d09bc8e01a281886d82e8d95ab363d864 100644 --- a/paddle/fluid/imperative/tracer.cc +++ b/paddle/fluid/imperative/tracer.cc @@ -18,11 +18,13 @@ #include #include #include +#include #include "paddle/fluid/framework/var_type_inference.h" #include "paddle/fluid/operators/math/math_function.h" #include "paddle/fluid/platform/device_context.h" #include "paddle/fluid/platform/enforce.h" +#include "paddle/fluid/platform/profiler.h" namespace paddle { namespace imperative { @@ -44,25 +46,25 @@ void CreateGradOp(const framework::OpDesc& op_desc, } } -void InitGrad(VarBase* var, platform::DeviceContext* dev_ctx) { +void CreateNoBuffuerGrad(std::shared_ptr var, + platform::DeviceContext* dev_ctx) { PADDLE_ENFORCE_NOT_NULL(var, "Could not get valid var base"); PADDLE_ENFORCE_NOT_NULL(dev_ctx, "Could not get valid device from forward op"); if (var->grads_ == nullptr) { auto& var_t = var->var_->Get(); - var->grads_ = new VarBase(var->GradName(), framework::proto::VarType::FP32, - framework::vectorize(var_t.dims()), - dev_ctx->GetPlace(), true, false); - auto grad_t = var->grads_->var_->GetMutable(); - operators::math::set_constant(*dev_ctx, grad_t, 0.0); + var->grads_ = std::shared_ptr( + new VarBase(var->GradName(), framework::proto::VarType::FP32, + framework::vectorize(var_t.dims()), dev_ctx->GetPlace(), + var->IsStopGradient(), false, false)); } } platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs) { platform::Place result = place; - for (auto it : inputs) { - for (VarBase* var : it.second) { + for (const auto& it : inputs) { + for (const std::shared_ptr& var : it.second) { platform::Place tmp_place = var->var_->Get().place(); if (!platform::is_same_place(tmp_place, result)) { @@ -96,7 +98,7 @@ framework::VariableNameMap CreateInputVarNameMap( auto var_vector = it->second; std::vector args; args.reserve(var_vector.size()); - for (VarBase* var_base : var_vector) { + for (std::shared_ptr var_base : var_vector) { args.emplace_back(var_base->Name()); } result[in.name()] = args; @@ -124,7 +126,7 @@ framework::VariableNameMap CreateOutputVarNameMap( auto var_vector = it->second; std::vector args; args.reserve(var_vector.size()); - for (VarBase* var_base : var_vector) { + for (const std::shared_ptr& var_base : var_vector) { args.emplace_back(var_base->Name()); } result[out.name()] = args; @@ -135,25 +137,24 @@ framework::VariableNameMap CreateOutputVarNameMap( Tracer::Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {} -std::set Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, - VarBasePtrMap* outputs, - framework::AttributeMap attrs_map, - const platform::Place expected_place, - const bool stop_gradient) { +void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, + VarBasePtrMap* outputs, framework::AttributeMap attrs_map, + const platform::Place expected_place, + const bool stop_gradient) { + platform::RecordEvent record_event(op->type_); framework::VariableValueMap invars_map; framework::VariableValueMap outvars_map; // Construct input_vars_map and output_vars_map - std::map current_vars_map; - op->input_vars_ = inputs; - for (auto it : op->input_vars_) { + std::map> current_vars_map; + for (auto it : inputs) { auto& invars = invars_map[it.first]; invars.reserve(it.second.size()); - for (VarBase* inp : it.second) { + for (std::shared_ptr inp : it.second) { PADDLE_ENFORCE_NOT_NULL(inp->var_, "op %s input %s nullptr", op->Type(), inp->Name()); - invars.emplace_back(inp->var_); + invars.emplace_back(inp->var_.get()); if (!stop_gradient) { current_vars_map[inp->Name()] = inp; } @@ -164,14 +165,16 @@ std::set Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, op->TrackPreOp(it.first, it.second); } - op->output_vars_ = *outputs; - for (auto it : op->output_vars_) { + for (const auto& it : *outputs) { auto& outvars = outvars_map[it.first]; - const std::vector& outputs = it.second; - outvars.reserve(outputs.size()); - for (size_t i = 0U; i < outputs.size(); ++i) { - VarBase* out = outputs[i]; - outvars.emplace_back(out->var_); + const std::vector>& outputs_tmp = + it.second; + outvars.reserve(outputs_tmp.size()); + for (size_t i = 0U; i < outputs_tmp.size(); ++i) { + // Add weak_ptr to track outputs + op->outputs_ref.emplace_back(outputs_tmp[i]); + std::shared_ptr out = outputs_tmp[i]; + outvars.emplace_back(out->var_.get()); out->TrackPreOp(op, it.first, i, stop_gradient); if (!stop_gradient) { current_vars_map[out->Name()] = out; @@ -222,8 +225,6 @@ std::set Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, framework::ExecutionContext(prepared_op.op, scope, *prepared_op.dev_ctx, prepared_op.ctx, prepared_op.kernel_configs)); - // construct backward op - std::set vars_saved_for_backward; if (!stop_gradient) { VLOG(5) << "start construct backward op"; @@ -257,13 +258,13 @@ std::set Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, // Forward inputs or outputs. grad_in_vars.emplace_back(fwd_var_it->second); } else { - VarBase* var = current_vars_map[var_it->second]; - InitGrad(var, prepared_op.GetDeviceContext()); + std::shared_ptr var = + current_vars_map[var_it->second]; + CreateNoBuffuerGrad(var, prepared_op.GetDeviceContext()); // Douts. + var->grads_->SetPreOp(var->PreOp()); grad_in_vars.emplace_back(var->grads_); } - - vars_saved_for_backward.insert(it.first); } } @@ -275,70 +276,17 @@ std::set Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs, "Could not found the grad op output var, should this " "operator %s's stop gradient be True", op->Type()); - VarBase* var = current_vars_map[var_it->second]; - InitGrad(var, prepared_op.GetDeviceContext()); + + std::shared_ptr var = + current_vars_map[var_it->second]; + CreateNoBuffuerGrad(var, prepared_op.GetDeviceContext()); + var->grads_->SetPreOp(var->PreOp()); grad_out_vars.push_back(var->grads_); VLOG(3) << "grads output var name: " << var->name_; } } } } - - return vars_saved_for_backward; } - -std::vector Tracer::PyTrace(OpBase* op, - const std::vector& inputs, - bool stop_gradient) { - VLOG(3) << "py_trace " << op->Type(); - - op->input_vars_[PyLayer::kFwdInp] = inputs; - - std::vector ret_vars = - PyLayer::Apply(op->forward_id_, inputs); - - op->TrackPreOp(PyLayer::kFwdInp, inputs); - - std::vector& outputs = op->output_vars_[PyLayer::kFwdOut]; - outputs.reserve(ret_vars.size()); - for (size_t i = 0U; i != ret_vars.size(); ++i) { - framework::Variable* v = ret_vars[i]; - VarBase* out = new VarBase(string::Sprintf("%s_out_%d", op->Type(), i), v, - nullptr, stop_gradient); - outputs.emplace_back(out); - out->TrackPreOp(op, PyLayer::kFwdOut, i, stop_gradient); - } - - if (!stop_gradient) { - VLOG(5) << "start construct backward op"; - op->grad_input_vars_.resize(1); - op->grad_output_vars_.resize(1); - auto& grad_input_vars = - op->grad_input_vars_[0][framework::GradVarName(PyLayer::kFwdInp)]; - auto& grad_output_vars = - op->grad_output_vars_[0][framework::GradVarName(PyLayer::kFwdOut)]; - - for (VarBase* inp : inputs) { - grad_input_vars.push_back(inp); - } - for (VarBase* out : outputs) { - grad_input_vars.push_back(out); - } - - // TODO(minqiyang): Add GPU support for PyLayer, only support CPU now - platform::CPUPlace place; - for (VarBase* out : outputs) { - InitGrad(out, platform::DeviceContextPool::Instance().Get(place)); - grad_input_vars.push_back(out->grads_); - } - - for (VarBase* inp : inputs) { - InitGrad(inp, platform::DeviceContextPool::Instance().Get(place)); - grad_output_vars.push_back(inp->grads_); - } - } - return outputs; -} - } // namespace imperative } // namespace paddle diff --git a/paddle/fluid/imperative/tracer.h b/paddle/fluid/imperative/tracer.h index a87f3b8009dd552626c6c03fba3b0bbf3a78bb83..02d902274103e1d42db7b849da633bf50a6167ad 100644 --- a/paddle/fluid/imperative/tracer.h +++ b/paddle/fluid/imperative/tracer.h @@ -36,9 +36,6 @@ void CreateGradOp(const framework::OpDesc& op_desc, framework::OpDesc** grad_op_desc, std::unordered_map* grad_to_var); -void InitVar(const VarBase* var, framework::Variable* grad_var, - platform::DeviceContext* dev_ctx); - platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs); class Tracer { @@ -47,14 +44,11 @@ class Tracer { virtual ~Tracer() {} - std::set Trace(OpBase* op, const VarBasePtrMap& inputs, - VarBasePtrMap* outputs, // NOLINT - framework::AttributeMap attrs_map, - const platform::Place expected_place, - const bool stop_gradient = false); - - std::vector PyTrace(OpBase* op, const std::vector& inputs, - bool stop_gradient = false); + void Trace(OpBase* op, const VarBasePtrMap& inputs, + VarBasePtrMap* outputs, // NOLINT + framework::AttributeMap attrs_map, + const platform::Place expected_place, + const bool stop_gradient = false); private: platform::Place GetPlace(const VarBasePtrMap& inputs); diff --git a/paddle/fluid/imperative/type_defs.h b/paddle/fluid/imperative/type_defs.h index c51ce931defbc87231a2f8c6c07f99d9853fb283..fab8c2e6b9102f6ccaea09a5c08df9574f6b6a56 100644 --- a/paddle/fluid/imperative/type_defs.h +++ b/paddle/fluid/imperative/type_defs.h @@ -15,7 +15,10 @@ limitations under the License. */ #pragma once #include +#include #include +#include +#include #include namespace paddle { @@ -24,9 +27,17 @@ namespace imperative { class VarBase; class OpBase; -typedef std::map> VarBasePtrMap; -typedef std::map> ConstVarBasePtrMap; +typedef std::map>> + VarBasePtrMap; +typedef std::vector> VarBaseWeakPtrList; typedef std::map> OpBasePtrMap; +typedef std::unordered_map< + const VarBase*, + std::pair>>>> + BackwardSumMap; // var_grad -> {place, {id -> var_grad@rename}} +typedef std::unordered_map> GradientRef; +// var_grad -> {ref_times, is_first_to_be_accumulate} } // namespace imperative } // namespace paddle diff --git a/paddle/fluid/inference/CMakeLists.txt b/paddle/fluid/inference/CMakeLists.txt index 5e0be5d445eae9d6d857ab0d6c5816807b4af523..44eaf90371d58d94737c871e973e17bcc96ad343 100644 --- a/paddle/fluid/inference/CMakeLists.txt +++ b/paddle/fluid/inference/CMakeLists.txt @@ -17,7 +17,7 @@ if (TENSORRT_FOUND) add_subdirectory(tensorrt) endif() -if (ANAKIN_FOUND) +if (ANAKIN_SUBGRAPH) add_subdirectory(anakin) endif() @@ -43,11 +43,15 @@ if(WITH_MKLDNN) endif() set(STATIC_INFERENCE_APIS paddle_fluid_api paddle_inference_api analysis_predictor) +if (ANAKIN_FOUND) + set(ANAKIN_SHARED_INFERENCE_SRCS ${CMAKE_CURRENT_SOURCE_DIR}/api/api_anakin_engine.cc) +endif() set(SHARED_INFERENCE_SRCS io.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api_impl.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/analysis_predictor.cc ${mkldnn_quantizer_src} - ${CMAKE_CURRENT_SOURCE_DIR}/api/details/zero_copy_tensor.cc) + ${CMAKE_CURRENT_SOURCE_DIR}/api/details/zero_copy_tensor.cc + ${ANAKIN_SHARED_INFERENCE_SRCS}) if(WIN32) sep_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor reset_tensor_array diff --git a/paddle/fluid/inference/anakin/convert/CMakeLists.txt b/paddle/fluid/inference/anakin/convert/CMakeLists.txt index 5d85525a652a6016694e012853c95aca086b3fd9..67194c9ff2411bb51bf3db8c8d0d38d8be3d576b 100644 --- a/paddle/fluid/inference/anakin/convert/CMakeLists.txt +++ b/paddle/fluid/inference/anakin/convert/CMakeLists.txt @@ -5,19 +5,19 @@ detection_out.cc scale.cc dropout.cc im2sequence.cc sum.cc affine_channel.cc roi_align.cc shuffle_channel.cc helper.cc DEPS anakin_engine framework_proto scope op_registry gtest) -cc_test(test_anakin_fc SRCS test_fc_op.cc DEPS anakin_op_converter mul_op SERIAL) -cc_test(test_anakin_conv2d SRCS test_conv2d_op.cc DEPS anakin_op_converter conv_op im2col vol2col depthwise_conv SERIAL) -cc_test(test_anakin_activation SRCS test_activation_op.cc DEPS activation_op anakin_op_converter SERIAL) -cc_test(test_anakin_pool2d SRCS test_pool2d_op.cc DEPS anakin_op_converter pool_op pooling SERIAL) -cc_test(test_anakin_concat SRCS test_concat_op.cc DEPS anakin_op_converter concat_op concat_and_split SERIAL) -cc_test(test_anakin_split SRCS test_split_op.cc DEPS anakin_op_converter split_op concat_and_split SERIAL) -cc_test(test_anakin_elementwise SRCS test_elementwise_op.cc DEPS anakin_op_converter elementwise_add_op elementwise_mul_op SERIAL) -cc_test(test_anakin_relu SRCS test_relu_op.cc DEPS activation_op anakin_op_converter SERIAL SERIAL) -cc_test(test_anakin_softmax SRCS test_softmax_op.cc DEPS anakin_op_converter softmax_op softmax SERIAL) -cc_test(test_anakin_reshape SRCS test_reshape_op.cc DEPS anakin_op_converter reshape_op SERIAL) -cc_test(test_anakin_flatten SRCS test_flatten_op.cc DEPS anakin_op_converter flatten_op reshape_op SERIAL) -cc_test(test_anakin_transpose SRCS test_transpose_op.cc DEPS anakin_op_converter transpose_op SERIAL) -cc_test(test_anakin_batch_norm SRCS test_batch_norm_op.cc DEPS anakin_op_converter batch_norm_op SERIAL) -cc_test(test_anakin_dropout SRCS test_dropout_op.cc DEPS anakin_op_converter dropout_op SERIAL) -cc_test(test_anakin_sum SRCS test_sum_op.cc DEPS anakin_op_converter sum_op selected_rows_functor SERIAL) -cc_test(test_anakin_affine_channel SRCS test_affine_channel_op.cc DEPS anakin_op_converter affine_channel_op SERIAL) +cc_test(test_anakin_fc SRCS test_fc_op.cc DEPS anakin_op_converter mul_op) +cc_test(test_anakin_conv2d SRCS test_conv2d_op.cc DEPS anakin_op_converter conv_op im2col vol2col depthwise_conv) +cc_test(test_anakin_activation SRCS test_activation_op.cc DEPS activation_op anakin_op_converter) +cc_test(test_anakin_pool2d SRCS test_pool2d_op.cc DEPS anakin_op_converter pool_op pooling) +cc_test(test_anakin_concat SRCS test_concat_op.cc DEPS anakin_op_converter concat_op concat_and_split) +cc_test(test_anakin_split SRCS test_split_op.cc DEPS anakin_op_converter split_op concat_and_split) +cc_test(test_anakin_elementwise SRCS test_elementwise_op.cc DEPS anakin_op_converter elementwise_add_op elementwise_mul_op) +cc_test(test_anakin_relu SRCS test_relu_op.cc DEPS activation_op anakin_op_converter) +cc_test(test_anakin_softmax SRCS test_softmax_op.cc DEPS anakin_op_converter softmax_op softmax) +cc_test(test_anakin_reshape SRCS test_reshape_op.cc DEPS anakin_op_converter reshape_op) +cc_test(test_anakin_flatten SRCS test_flatten_op.cc DEPS anakin_op_converter flatten_op reshape_op) +cc_test(test_anakin_transpose SRCS test_transpose_op.cc DEPS anakin_op_converter transpose_op) +cc_test(test_anakin_batch_norm SRCS test_batch_norm_op.cc DEPS anakin_op_converter batch_norm_op) +cc_test(test_anakin_dropout SRCS test_dropout_op.cc DEPS anakin_op_converter dropout_op) +cc_test(test_anakin_sum SRCS test_sum_op.cc DEPS anakin_op_converter sum_op selected_rows_functor) +cc_test(test_anakin_affine_channel SRCS test_affine_channel_op.cc DEPS anakin_op_converter affine_channel_op) diff --git a/paddle/fluid/inference/anakin/convert/conv2d.cc b/paddle/fluid/inference/anakin/convert/conv2d.cc index 70e0adf5ead45dc93c31f5d8aecffd7213b35954..26f78efa61cbd984978f830277340d53c3895d67 100644 --- a/paddle/fluid/inference/anakin/convert/conv2d.cc +++ b/paddle/fluid/inference/anakin/convert/conv2d.cc @@ -70,7 +70,8 @@ void Conv2dOpConverter::operator()( if (enable_int8) { const float int8_range = 127.; float in_scale = boost::get(op_desc.GetAttr("input_scale")); - float weight_scale = boost::get(op_desc.GetAttr("weight_scale")); + auto weight_scale = + boost::get>(op_desc.GetAttr("weight_scale")); PBlock *weight1 = new PBlock(anakin_shape, ::anakin::AK_INT8); this->engine_->RegistBlock(weight1); @@ -91,8 +92,8 @@ void Conv2dOpConverter::operator()( weight1->d_tensor().copy_from(weight1->h_tensor()); this->engine_->AddOpAttr(op_name, "weight_1", *weight1); this->engine_->Graph()->SetOpPrec(op_name, ::anakin::AK_INT8); - this->engine_->Graph()->SetWeightsScale(op_name, - {weight_scale / int8_range}, false); + this->engine_->Graph()->SetWeightsScale( + op_name, {weight_scale[0] / int8_range}, false); this->engine_->AddTensorScale(input_name, in_scale / int8_range); } else { auto *weight1 = pblock_from_tensor( diff --git a/paddle/fluid/inference/anakin/convert/conv2d_fusion.cc b/paddle/fluid/inference/anakin/convert/conv2d_fusion.cc index a1568b8bdeeb93790ecc5f37844e7bf4b8892993..f2e6003aa6849fce57111fadf88fb27bcc95f42e 100644 --- a/paddle/fluid/inference/anakin/convert/conv2d_fusion.cc +++ b/paddle/fluid/inference/anakin/convert/conv2d_fusion.cc @@ -72,7 +72,8 @@ void Conv2dFusionOpConverter::operator()( if (enable_int8) { const float int8_range = 127.; float in_scale = boost::get(op_desc.GetAttr("input_scale")); - float weight_scale = boost::get(op_desc.GetAttr("weight_scale")); + auto weight_scale = + boost::get>(op_desc.GetAttr("weight_scale")); PBlock *weight1 = new PBlock(anakin_shape, ::anakin::AK_INT8); this->engine_->RegistBlock(weight1); @@ -93,8 +94,8 @@ void Conv2dFusionOpConverter::operator()( weight1->d_tensor().copy_from(weight1->h_tensor()); this->engine_->AddOpAttr(op_name, "weight_1", *weight1); this->engine_->Graph()->SetOpPrec(op_name, ::anakin::AK_INT8); - this->engine_->Graph()->SetWeightsScale(op_name, - {weight_scale / int8_range}, false); + this->engine_->Graph()->SetWeightsScale( + op_name, {weight_scale[0] / int8_range}, false); this->engine_->AddTensorScale(input_name, in_scale / int8_range); } else { auto weight_tensor = tensor_from_var(*filter_v, platform::CPUPlace()); diff --git a/paddle/fluid/inference/anakin/convert/elementwise.cc b/paddle/fluid/inference/anakin/convert/elementwise.cc index dd32baa0b90018c8e0175fa9cae85a9fbeccedf0..d221f26e11934d6851972d304c6f859346fe3b61 100644 --- a/paddle/fluid/inference/anakin/convert/elementwise.cc +++ b/paddle/fluid/inference/anakin/convert/elementwise.cc @@ -60,7 +60,7 @@ void ElementwiseMulOpConverter::operator()( auto op_name = op_desc.Type() + ":" + op_desc.Output("Out").front(); this->engine_->AddOp(op_name, "Eltwise", {x_name, y_name}, {out_name}); - std::string elementwise_type = "Prod"; + std::string elementwise_type = "Mul"; this->engine_->template AddOpAttr(op_name, "type", elementwise_type); std::vector coeff = {1.0, 1.0}; diff --git a/paddle/fluid/inference/anakin/convert/fc.cc b/paddle/fluid/inference/anakin/convert/fc.cc index 0621e3377b34660e3c2f1d1b83847bd46b5bd26d..b64d0b84fd45fe041c18721968fbc6ce8a794af5 100644 --- a/paddle/fluid/inference/anakin/convert/fc.cc +++ b/paddle/fluid/inference/anakin/convert/fc.cc @@ -76,7 +76,8 @@ void FcBaseOpConverter::operator()( ::anakin::saber::Shape anakin_shape(weight_shape); const float int8_range = 127.; float in_scale = boost::get(op_desc.GetAttr("input_scale")); - float weight_scale = boost::get(op_desc.GetAttr("weight_scale")); + auto weight_scale = + boost::get>(op_desc.GetAttr("weight_scale")); PBlock *weight1 = new PBlock(anakin_shape, ::anakin::AK_INT8); this->engine_->RegistBlock(weight1); @@ -95,8 +96,8 @@ void FcBaseOpConverter::operator()( weight1->d_tensor().copy_from(weight1->h_tensor()); this->engine_->AddOpAttr(op_name, "weight_1", *weight1); this->engine_->Graph()->SetOpPrec(op_name, ::anakin::AK_INT8); - this->engine_->Graph()->SetWeightsScale(op_name, - {weight_scale / int8_range}, false); + this->engine_->Graph()->SetWeightsScale( + op_name, {weight_scale[0] / int8_range}, false); this->engine_->AddTensorScale(input_name, in_scale / int8_range); } else { auto *weight1 = pblock_from_vector(trans_weight_data, diff --git a/paddle/fluid/inference/anakin/convert/op_converter.h b/paddle/fluid/inference/anakin/convert/op_converter.h index a6ae51bd4b1c67104c732e12a66f74d7e4580bb5..1058e744bca9cc1c01471ec50fa26eabae99220d 100644 --- a/paddle/fluid/inference/anakin/convert/op_converter.h +++ b/paddle/fluid/inference/anakin/convert/op_converter.h @@ -153,11 +153,12 @@ template class AnakinOpConverter<::anakin::saber::NV, ::anakin::Precision::FP32>; template class AnakinOpConverter<::anakin::saber::NV, ::anakin::Precision::INT8>; - +#ifdef ANAKIN_X86_PLACE template class AnakinOpConverter<::anakin::saber::X86, ::anakin::Precision::FP32>; template class AnakinOpConverter<::anakin::saber::X86, ::anakin::Precision::INT8>; +#endif } // namespace anakin } // namespace inference } // namespace paddle @@ -203,16 +204,16 @@ template class AnakinOpConverter<::anakin::saber::X86, CPU, ::anakin::saber::X86, precision_type__, \ ::anakin::Precision::precision_type__) -#ifdef PADDLE_WITH_CUDA +#if defined(PADDLE_WITH_CUDA) && defined(ANAKIN_X86_PLACE) #define REGISTER_ANAKIN_OP_CONVERTER(op_type__, Converter__) \ REGISTER_CUDA_ANAKIN_OP_CONVERTER(op_type__, Converter__, FP32); \ REGISTER_CUDA_ANAKIN_OP_CONVERTER(op_type__, Converter__, INT8); \ REGISTER_CPU_ANAKIN_OP_CONVERTER(op_type__, Converter__, FP32); \ REGISTER_CPU_ANAKIN_OP_CONVERTER(op_type__, Converter__, INT8) -#else -#define REGISTER_ANAKIN_OP_CONVERTER(op_type__, Converter__) \ - REGISTER_CPU_ANAKIN_OP_CONVERTER(op_type__, Converter__, FP32); \ - REGISTER_CPU_ANAKIN_OP_CONVERTER(op_type__, Converter__, INT8) +#elif defined(PADDLE_WITH_CUDA) +#define REGISTER_ANAKIN_OP_CONVERTER(op_type__, Converter__) \ + REGISTER_CUDA_ANAKIN_OP_CONVERTER(op_type__, Converter__, FP32); \ + REGISTER_CUDA_ANAKIN_OP_CONVERTER(op_type__, Converter__, INT8) #endif #define USE_ANAKIN_CONVERTER_BASE(op_type__, place_type__, precision_type__) \ @@ -221,12 +222,16 @@ template class AnakinOpConverter<::anakin::saber::X86, __attribute__((unused)) = \ Touch_anakin_##op_type__##_##place_type__##_##precision_type__(); +#if defined(PADDLE_WITH_CUDA) && defined(ANAKIN_X86_PLACE) +#define USE_ANAKIN_CONVERTER(op_type__) \ + USE_ANAKIN_CONVERTER_BASE(op_type__, CUDA, FP32) \ + USE_ANAKIN_CONVERTER_BASE(op_type__, CPU, FP32) +#define USE_INT8_ANAKIN_CONVERTER(op_type__) \ + USE_ANAKIN_CONVERTER_BASE(op_type__, CUDA, INT8) \ + USE_ANAKIN_CONVERTER_BASE(op_type__, CPU, INT8) +#elif defined(PADDLE_WITH_CUDA) #define USE_ANAKIN_CONVERTER(op_type__) \ USE_ANAKIN_CONVERTER_BASE(op_type__, CUDA, FP32) #define USE_INT8_ANAKIN_CONVERTER(op_type__) \ USE_ANAKIN_CONVERTER_BASE(op_type__, CUDA, INT8) - -#define USE_CPU_ANAKIN_CONVERTER(op_type__) \ - USE_ANAKIN_CONVERTER_BASE(op_type__, CPU, FP32) -#define USE_CPU_INT8_ANAKIN_CONVERTER(op_type__) \ - USE_ANAKIN_CONVERTER_BASE(op_type__, CPU, INT8) +#endif diff --git a/paddle/fluid/inference/anakin/convert/test_activation_op.cc b/paddle/fluid/inference/anakin/convert/test_activation_op.cc index 4f898252d2798022d09f65e03b3cde35fcb6730c..5ac8b45882f5175f90db6c5ddb2f41a67ca145e2 100644 --- a/paddle/fluid/inference/anakin/convert/test_activation_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_activation_op.cc @@ -77,32 +77,6 @@ TEST(swish_op, gpu) { } #endif -/* -TEST(sigm_op, cpu) { - platform::CPUPlace cpu_place; - platform::CPUDeviceContext ctx(cpu_place); - test_activation_op<::anakin::saber::X86>("sigmoid", ctx, false); -} - -TEST(tanh_op, cpu) { - platform::CPUPlace cpu_place; - platform::CPUDeviceContext ctx(cpu_place); - test_activation_op<::anakin::saber::X86>("tanh", ctx, false); -} - -TEST(relu6_op, cpu) { - platform::CPUPlace cpu_place; - platform::CPUDeviceContext ctx(cpu_place); - test_activation_op<::anakin::saber::X86>("relu6", ctx, false); -} - -TEST(swish_op, cpu) { - platform::CPUPlace cpu_place; - platform::CPUDeviceContext ctx(cpu_place); - test_activation_op<::anakin::saber::X86>("swish", ctx, false); -} -*/ - } // namespace anakin } // namespace inference } // namespace paddle @@ -112,13 +86,7 @@ USE_OP(tanh); USE_OP(relu6); USE_OP(swish); -USE_CPU_ANAKIN_CONVERTER(sigmoid); -USE_CPU_ANAKIN_CONVERTER(tanh); -USE_CPU_ANAKIN_CONVERTER(relu6); -USE_CPU_ANAKIN_CONVERTER(swish); -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(sigmoid); USE_ANAKIN_CONVERTER(tanh); USE_ANAKIN_CONVERTER(relu6); USE_ANAKIN_CONVERTER(swish); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_affine_channel_op.cc b/paddle/fluid/inference/anakin/convert/test_affine_channel_op.cc index f6399387aa264d993462d33011a4cddaa4a23359..008537dc8a5a82326f243e73fc33ce1dbeb730ef 100644 --- a/paddle/fluid/inference/anakin/convert/test_affine_channel_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_affine_channel_op.cc @@ -57,19 +57,16 @@ TEST(affine_channel_op, gpu) { test_affine_channel_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(affine_channel_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_affine_channel_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(affine_channel); -USE_CPU_ANAKIN_CONVERTER(affine_channel); -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(affine_channel); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_batch_norm_op.cc b/paddle/fluid/inference/anakin/convert/test_batch_norm_op.cc index c008ef1bd5ee258b7d3095ac7836a9eacb2cf83a..edba90235fac023a1c9712f308b535da9ba39e3a 100644 --- a/paddle/fluid/inference/anakin/convert/test_batch_norm_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_batch_norm_op.cc @@ -73,19 +73,15 @@ TEST(batch_norm_op, gpu) { test_batchnorm_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(batch_norm_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_batchnorm_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(batch_norm); -USE_CPU_ANAKIN_CONVERTER(batch_norm); - -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(batch_norm); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_concat_op.cc b/paddle/fluid/inference/anakin/convert/test_concat_op.cc index 42dfbeb5cdc4062143385bde569c3d80f1c774c9..6870260c865873874072e25721edafeba8dab234 100644 --- a/paddle/fluid/inference/anakin/convert/test_concat_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_concat_op.cc @@ -53,19 +53,15 @@ TEST(concat_op, gpu) { test_concat_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(concat_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_concat_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(concat); -USE_CPU_ANAKIN_CONVERTER(concat); - -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(concat); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_conv2d_op.cc b/paddle/fluid/inference/anakin/convert/test_conv2d_op.cc index e95e11c4f968814fb225dee8a7750a47ee6a976e..723a348b12e3b451b047514838a68e56238956a2 100644 --- a/paddle/fluid/inference/anakin/convert/test_conv2d_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_conv2d_op.cc @@ -60,20 +60,16 @@ TEST(conv2d_op, gpu) { test_conv2d_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(conv2d_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_conv2d_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(conv2d); -USE_CPU_ANAKIN_CONVERTER(conv2d); - -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(conv2d); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_dropout_op.cc b/paddle/fluid/inference/anakin/convert/test_dropout_op.cc index ae27e27ded5d9252e85c0da5d24b777e1a189b63..83792676a00440257d836c3fb50e7d685f5d110a 100644 --- a/paddle/fluid/inference/anakin/convert/test_dropout_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_dropout_op.cc @@ -54,19 +54,16 @@ TEST(dropout_op, gpu) { test_dropout_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(dropout_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_dropout_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(dropout); -USE_CPU_ANAKIN_CONVERTER(dropout); -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(dropout); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_elementwise_op.cc b/paddle/fluid/inference/anakin/convert/test_elementwise_op.cc index bff75294908aab2997fbb1138a53112f22afe312..ee128c1ec9ad62998310e7faaef962fa251cca7f 100644 --- a/paddle/fluid/inference/anakin/convert/test_elementwise_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_elementwise_op.cc @@ -59,29 +59,23 @@ TEST(elementwise_op, native_mul_gpu) { test_elementwise_op<::anakin::saber::NV>("elementwise_mul", ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(elementwise_op, native_add_cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_elementwise_op<::anakin::saber::X86>("elementwise_add", ctx, false); } - TEST(elementwise_op, native_mul_cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_elementwise_op<::anakin::saber::X86>("elementwise_mul", ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(elementwise_add); USE_OP(elementwise_mul); -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(elementwise_add); USE_ANAKIN_CONVERTER(elementwise_mul); -#endif - -USE_CPU_ANAKIN_CONVERTER(elementwise_add); -USE_CPU_ANAKIN_CONVERTER(elementwise_mul); diff --git a/paddle/fluid/inference/anakin/convert/test_fc_op.cc b/paddle/fluid/inference/anakin/convert/test_fc_op.cc index a24c809c0221322256d985bc5c71c97afe9bd31f..3e68d8fed6a66423d5fc4c271445a41207417253 100644 --- a/paddle/fluid/inference/anakin/convert/test_fc_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_fc_op.cc @@ -49,19 +49,16 @@ TEST(mul_op, gpu) { test_mul_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(mul_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_mul_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(mul); -USE_CPU_ANAKIN_CONVERTER(fc); -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(fc); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_flatten_op.cc b/paddle/fluid/inference/anakin/convert/test_flatten_op.cc index 5765f5ebd1f2a0c3adaee95f796273d51284f9e1..5e4cfdabfd7ca4dfc865ba3722030c5dbd44d036 100644 --- a/paddle/fluid/inference/anakin/convert/test_flatten_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_flatten_op.cc @@ -48,20 +48,17 @@ TEST(flatten_op, gpu) { test_flatten_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(flatten_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_flatten_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(reshape); USE_OP_ITSELF(flatten); -USE_CPU_ANAKIN_CONVERTER(flatten); -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(flatten); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_pool2d_op.cc b/paddle/fluid/inference/anakin/convert/test_pool2d_op.cc index 90503b1fbba81eb20b5d32bca32ab109245fc07c..9b23b5b93df16ae833fda891dc89c8dfe98cddcb 100644 --- a/paddle/fluid/inference/anakin/convert/test_pool2d_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_pool2d_op.cc @@ -87,7 +87,7 @@ TEST(Pool2dOpConverter, avg_ceil_test) { test_pool2d<::anakin::saber::NV>(ctx, true, false, true, "avg"); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(Pool2dOpConverter, normal_cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); @@ -110,14 +110,10 @@ TEST(Pool2dOpConverter, avg_ceil_test_cpu) { platform::CPUDeviceContext ctx(cpu_place); test_pool2d<::anakin::saber::X86>(ctx, false, false, true, "avg"); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(pool2d); -USE_CPU_ANAKIN_CONVERTER(pool2d); - -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(pool2d); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_relu_op.cc b/paddle/fluid/inference/anakin/convert/test_relu_op.cc index 3f224796519650a4a26907f9db58c5c8aab56e4f..eb6429f3383d2848a8b512009ada78d578dab919 100644 --- a/paddle/fluid/inference/anakin/convert/test_relu_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_relu_op.cc @@ -66,10 +66,5 @@ TEST(leaky_relu_op, gpu) { USE_OP(relu); USE_OP(leaky_relu); -USE_CPU_ANAKIN_CONVERTER(relu); -USE_CPU_ANAKIN_CONVERTER(leaky_relu); - -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(relu); USE_ANAKIN_CONVERTER(leaky_relu); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_reshape_op.cc b/paddle/fluid/inference/anakin/convert/test_reshape_op.cc index e102bd3ac3ea0d5e0ec0fd46b243f38c13af9580..b1be42e542ce06cb1ea88af8db71f2dfcec8123b 100644 --- a/paddle/fluid/inference/anakin/convert/test_reshape_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_reshape_op.cc @@ -81,7 +81,7 @@ TEST(reshape2_op, gpu) { test_reshape2_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(reshape1_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); @@ -93,14 +93,10 @@ TEST(reshape2_op, cpu) { platform::CPUDeviceContext ctx(cpu_place); test_reshape2_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(reshape); -USE_CPU_ANAKIN_CONVERTER(reshape); - -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(reshape); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_softmax_op.cc b/paddle/fluid/inference/anakin/convert/test_softmax_op.cc index de0b18fdbfd5f721fcd397a621bcee04ff5e5ae4..1a324739d98534d3b5443cd5f2c2f57f7045543e 100644 --- a/paddle/fluid/inference/anakin/convert/test_softmax_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_softmax_op.cc @@ -48,20 +48,16 @@ TEST(softmax_op, gpu) { test_softmax_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(relu_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_softmax_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(softmax); -USE_CPU_ANAKIN_CONVERTER(softmax); - -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(softmax); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_split_op.cc b/paddle/fluid/inference/anakin/convert/test_split_op.cc index 9a42ffd853bb071cfa1a21b17450124dc46f8211..f9ef54fdcacecd7540becb5b8aff997d40c5872d 100644 --- a/paddle/fluid/inference/anakin/convert/test_split_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_split_op.cc @@ -92,7 +92,7 @@ TEST(split_op, test_different_shape_axis3_batch1) { platform::CUDADeviceContext ctx(gpu_place); AnakinSliceTest<::anakin::saber::NV, 3>(ctx, true, {1, 3, 2, 3}, {2, 1}); } - +#ifdef ANAKIN_X86_PLACE TEST(split_op, test_different_shape_axis1_batch1_cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); @@ -110,13 +110,10 @@ TEST(split_op, test_different_shape_axis3_batch1_cpu) { platform::CPUDeviceContext ctx(cpu_place); AnakinSliceTest<::anakin::saber::X86, 3>(ctx, false, {1, 3, 2, 4}, {2, 2}); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(split); -USE_CPU_ANAKIN_CONVERTER(split); -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(split); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_sum_op.cc b/paddle/fluid/inference/anakin/convert/test_sum_op.cc index 65f67ebd129893f553dc5b1663dcea377653b463..9d26430ea68c5b818b96365e36381a088c3725f6 100644 --- a/paddle/fluid/inference/anakin/convert/test_sum_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_sum_op.cc @@ -49,19 +49,16 @@ TEST(sum_op, gpu) { test_sum_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(sum_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); test_sum_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(sum); -USE_CPU_ANAKIN_CONVERTER(sum); -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(sum); -#endif diff --git a/paddle/fluid/inference/anakin/convert/test_transpose_op.cc b/paddle/fluid/inference/anakin/convert/test_transpose_op.cc index 51b69dfbb08b7335590407fec2068baff1a8d046..466e2f1a49f21b30973553ae6cd7bd4f0864def3 100644 --- a/paddle/fluid/inference/anakin/convert/test_transpose_op.cc +++ b/paddle/fluid/inference/anakin/convert/test_transpose_op.cc @@ -79,7 +79,7 @@ TEST(transpose2_op, gpu) { test_transpose2_op<::anakin::saber::NV>(ctx, true); } #endif - +#ifdef ANAKIN_X86_PLACE TEST(transpose1_op, cpu) { platform::CPUPlace cpu_place; platform::CPUDeviceContext ctx(cpu_place); @@ -91,13 +91,10 @@ TEST(transpose2_op, cpu) { platform::CPUDeviceContext ctx(cpu_place); test_transpose2_op<::anakin::saber::X86>(ctx, false); } - +#endif } // namespace anakin } // namespace inference } // namespace paddle USE_OP(transpose); -USE_CPU_ANAKIN_CONVERTER(transpose); -#ifdef PADDLE_WITH_CUDA USE_ANAKIN_CONVERTER(transpose); -#endif diff --git a/paddle/fluid/inference/anakin/convert/ut_helper.h b/paddle/fluid/inference/anakin/convert/ut_helper.h index 2f8f953892c390c4790869d17981be6b9ae05d0f..92441f2560f3260c892f14c72d07d4f5bb2e6e7e 100644 --- a/paddle/fluid/inference/anakin/convert/ut_helper.h +++ b/paddle/fluid/inference/anakin/convert/ut_helper.h @@ -33,7 +33,6 @@ limitations under the License. */ #include "paddle/fluid/platform/enforce.h" using anakin::Precision; -using anakin::saber::X86; namespace paddle { namespace inference { @@ -215,13 +214,14 @@ class AnakinConvertValidation { template class AnakinConvertValidation<::anakin::saber::NV, ::anakin::Precision::FP32>; -template class AnakinConvertValidation<::anakin::saber::X86, - ::anakin::Precision::FP32>; - template class AnakinConvertValidation<::anakin::saber::NV, ::anakin::Precision::INT8>; +#ifdef ANAKIN_X86_PLACE +template class AnakinConvertValidation<::anakin::saber::X86, + ::anakin::Precision::FP32>; template class AnakinConvertValidation<::anakin::saber::X86, ::anakin::Precision::INT8>; +#endif } // namespace anakin } // namespace inference } // namespace paddle diff --git a/paddle/fluid/inference/anakin/engine.cc b/paddle/fluid/inference/anakin/engine.cc index 529a859458a9884a53778e7133ab121ed582a3fb..13f16c4c8982ef491234fbc36b4e418b419a3dc9 100644 --- a/paddle/fluid/inference/anakin/engine.cc +++ b/paddle/fluid/inference/anakin/engine.cc @@ -32,18 +32,25 @@ namespace paddle { namespace inference { namespace anakin { +template +extern std::once_flag + AnakinEngine::init_anakin_; + template AnakinEngine::AnakinEngine( bool need_summary, int device, int max_batch_size, std::map> max_input_shape, std::vector program_inputs, bool auto_config_layout) - : graph_(new AnakinGraphT()), - net_(new AnakinNetT(need_summary)) { - device_ = device; - max_batch_size_ = max_batch_size; - max_input_shape_ = max_input_shape; - program_inputs_ = program_inputs; - auto_config_layout_ = auto_config_layout; + : device_(device), + max_batch_size_(max_batch_size), + max_input_shape_(max_input_shape), + program_inputs_(program_inputs), + auto_config_layout_(auto_config_layout) { + ::anakin::TargetWrapper::set_device(device_); + std::call_once(init_anakin_, + [this]() { ::anakin::Env::env_init(); }); + graph_.reset(new AnakinGraphT()); + net_.reset(new AnakinNetT(need_summary)); } template @@ -102,7 +109,7 @@ void AnakinEngine::BindInput( anakin_input = net_->get_in(input.first); } anakin_input->reshape(fluid_input_shape); - ::anakin::saber::Tensor tmp_anakin_tensor(data, TargetT(), 0, + ::anakin::saber::Tensor tmp_anakin_tensor(data, TargetT(), device_, fluid_input_shape); anakin_input->copy_from(tmp_anakin_tensor); } @@ -186,14 +193,14 @@ template class AnakinEngine<::anakin::saber::NV, ::anakin::Precision::INT8>; template class AnakinEngineManager<::anakin::saber::NV, ::anakin::Precision::INT8>; #endif - +#ifdef ANAKIN_X86_PLACE template class AnakinEngine<::anakin::saber::X86, ::anakin::Precision::FP32>; template class AnakinEngineManager<::anakin::saber::X86, ::anakin::Precision::FP32>; template class AnakinEngine<::anakin::saber::X86, ::anakin::Precision::INT8>; template class AnakinEngineManager<::anakin::saber::X86, ::anakin::Precision::INT8>; - +#endif // template class AnakinEngine<::anakin::saber::X86, ::anakin::Precision::FP32>; } // namespace anakin } // namespace inference diff --git a/paddle/fluid/inference/anakin/engine.h b/paddle/fluid/inference/anakin/engine.h index fb40f56511ba255413d422f156f4265102616d03..e62bb82fd12405fcb93b16310f9197e7c5fd63b5 100644 --- a/paddle/fluid/inference/anakin/engine.h +++ b/paddle/fluid/inference/anakin/engine.h @@ -24,7 +24,9 @@ #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/inference/engine.h" #include "paddle/fluid/inference/utils/singleton.h" - +#ifdef EXIT // NOLINT +#undef EXIT // NOLINT +#endif // NOLINT #include "framework/core/net/net.h" #include "framework/core/types.h" #include "framework/graph/graph.h" @@ -114,12 +116,13 @@ class AnakinEngine { private: bool initialized_{false}; + int device_; int max_batch_size_; std::map> max_input_shape_; - int device_; + std::vector program_inputs_; std::unique_ptr graph_; std::unique_ptr net_; - std::vector program_inputs_; + static std::once_flag init_anakin_; std::unordered_map tensor_scales_; // Always be false in gpu mode but true in most cpu cases. bool auto_config_layout_; diff --git a/paddle/fluid/inference/anakin/test_anakin_engine.cc b/paddle/fluid/inference/anakin/test_anakin_engine.cc index 422f415a5db62d9408834f600f875d7825d44952..3c8a33ec60f8aa04e4b40eb260f4107281332a7d 100644 --- a/paddle/fluid/inference/anakin/test_anakin_engine.cc +++ b/paddle/fluid/inference/anakin/test_anakin_engine.cc @@ -22,7 +22,6 @@ limitations under the License. */ using anakin::AK_FLOAT; using anakin::Precision; using anakin::saber::NV; -using anakin::saber::X86; using anakin::saber::Shape; using anakin::PBlock; using anakin::PTuple; diff --git a/paddle/fluid/inference/analysis/CMakeLists.txt b/paddle/fluid/inference/analysis/CMakeLists.txt index 7a795bda820dc34f78f33191558fd6c0ccd2cb24..d79fb529092ded176a4ab17ffb7cf828edce07a1 100644 --- a/paddle/fluid/inference/analysis/CMakeLists.txt +++ b/paddle/fluid/inference/analysis/CMakeLists.txt @@ -23,18 +23,46 @@ cc_library(analysis SRCS cc_test(test_dot SRCS dot_tester.cc DEPS analysis) +function(inference_analysis_test_build TARGET) + if(WITH_TESTING) + set(options "") + set(oneValueArgs "") + set(multiValueArgs SRCS EXTRA_DEPS) + cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + inference_base_test_build(${TARGET} + SRCS ${analysis_test_SRCS} + DEPS analysis pass ${GLOB_PASS_LIB} ${analysis_test_EXTRA_DEPS}) + endif() +endfunction() + +function(inference_analysis_test_run TARGET) + if(WITH_TESTING) + set(options "") + set(oneValueArgs "") + set(multiValueArgs COMMAND ARGS) + cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + inference_base_test_run(${TARGET} + COMMAND ${analysis_test_COMMAND} + ARGS ${analysis_test_ARGS}) + endif() +endfunction() + function(inference_analysis_test TARGET) if(WITH_TESTING) set(options "") set(oneValueArgs "") set(multiValueArgs SRCS ARGS EXTRA_DEPS) cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) - inference_base_test(${TARGET} + inference_base_test_build(${TARGET} SRCS ${analysis_test_SRCS} - DEPS analysis pass ${GLOB_PASS_LIB} ${analysis_test_EXTRA_DEPS} - ARGS --inference_model_dir=${WORD2VEC_MODEL_DIR} ${analysis_test_ARGS}) + DEPS analysis pass ${GLOB_PASS_LIB} ${analysis_test_EXTRA_DEPS}) + inference_base_test_run(${TARGET} + COMMAND ${TARGET} + ARGS ${analysis_test_ARGS}) endif() endfunction(inference_analysis_test) -inference_analysis_test(test_analyzer SRCS analyzer_tester.cc - EXTRA_DEPS reset_tensor_array paddle_inference_api) +inference_analysis_test(test_analyzer + SRCS analyzer_tester.cc + EXTRA_DEPS reset_tensor_array paddle_inference_api + ARGS --inference_model_dir=${WORD2VEC_MODEL_DIR}) diff --git a/paddle/fluid/inference/analysis/argument.h b/paddle/fluid/inference/analysis/argument.h index 66e8d8b5287178fd00dba963a2f4011ce8d8e51e..7bcd1f01bfeea927d95b2d04617dfcc73cabee3d 100644 --- a/paddle/fluid/inference/analysis/argument.h +++ b/paddle/fluid/inference/analysis/argument.h @@ -63,6 +63,16 @@ struct Argument { using anakin_max_shape_t = std::map>; bool Has(const std::string& key) const { return valid_fields_.count(key); } + void PartiallyRelease() { + if (Has("model_program_path")) { + if (Has("model_from_memory") && model_from_memory()) { + model_program_path().clear(); + model_program_path().shrink_to_fit(); + model_params_path().clear(); + model_params_path().shrink_to_fit(); + } + } + } #define DECL_ARGUMENT_FIELD(field__, Field, type__) \ public: \ @@ -164,6 +174,7 @@ struct Argument { AnalysisConfig::Precision); DECL_ARGUMENT_FIELD(tensorrt_use_static_engine, TensorRtUseStaticEngine, bool); + DECL_ARGUMENT_FIELD(tensorrt_use_calib_mode, TensorRtUseCalibMode, bool); DECL_ARGUMENT_FIELD(anakin_max_input_shape, AnakinMaxInputShape, anakin_max_shape_t); diff --git a/paddle/fluid/inference/analysis/dot.h b/paddle/fluid/inference/analysis/dot.h index 1cb790f18229003d86adad6cd69e2fa88c02549b..4693729cb43d7a9df96b11c4bf3064a70d1db4c3 100644 --- a/paddle/fluid/inference/analysis/dot.h +++ b/paddle/fluid/inference/analysis/dot.h @@ -19,14 +19,11 @@ */ #pragma once +#include #include #include #include #include -// #include "paddle/fluid/lite/utils/logging.h" -// #ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK -#include -// #endif namespace paddle { namespace inference { diff --git a/paddle/fluid/inference/analysis/helper.cc b/paddle/fluid/inference/analysis/helper.cc index 4f5c50d0d6b9ac94130cb82fb342ae5ee592f2c0..008608c14c75cbc0ee37baa57c6f0cbebc5bc064 100644 --- a/paddle/fluid/inference/analysis/helper.cc +++ b/paddle/fluid/inference/analysis/helper.cc @@ -63,6 +63,18 @@ void SetAttr>(framework::proto::OpDesc *op, } } +template <> +void SetAttr>(framework::proto::OpDesc *op, + const std::string &name, + const std::vector &data) { + auto *attr = op->add_attrs(); + attr->set_name(name); + attr->set_type(paddle::framework::proto::AttrType::INTS); + for (const auto i : data) { + attr->add_ints(i); + } +} + } // namespace analysis } // namespace inference } // namespace paddle diff --git a/paddle/fluid/inference/analysis/ir_pass_manager.cc b/paddle/fluid/inference/analysis/ir_pass_manager.cc index 4714c30507c4c3f8978ec10f3b19fd3f8a3b3b3d..f290e6fce49a3ffa187bd62dfee8c736f76dd62b 100644 --- a/paddle/fluid/inference/analysis/ir_pass_manager.cc +++ b/paddle/fluid/inference/analysis/ir_pass_manager.cc @@ -38,9 +38,9 @@ IRPassManager::IRPassManager(Argument *argument) { ARGUMENT_CHECK_FIELD(argument, main_program); graph_ = std::unique_ptr(new Graph(argument->main_program())); if (argument->Has("scope")) { - graph_->Set(framework::ir::kParamScopeAttr, - new framework::Scope *( - const_cast(&argument->scope()))); + auto *scope_ptr = argument->scope_ptr(); + PADDLE_ENFORCE(scope_ptr); + graph_->SetNotOwned(framework::ir::kParamScopeAttr, scope_ptr); } ARGUMENT_CHECK_FIELD(argument, ir_analysis_passes); @@ -87,7 +87,10 @@ void IRPassManager::CreatePasses(Argument *argument, bool enable_int8 = argument->tensorrt_precision_mode() == AnalysisConfig::Precision::kInt8; + pass->Set("predictor_id", new int(argument->predictor_id())); + bool use_calib_mode = argument->tensorrt_use_calib_mode(); pass->Set("enable_int8", new bool(enable_int8)); + pass->Set("use_calib_mode", new bool(use_calib_mode)); bool use_static_engine = argument->tensorrt_use_static_engine(); bool model_from_memory = argument->model_from_memory(); @@ -110,7 +113,10 @@ void IRPassManager::CreatePasses(Argument *argument, pass->Set("engine_opt_info", new std::map( argument->engine_opt_info())); } - + if (pass_name == "ngraph_subgraph_pass") { + pass->Set("program", + new framework::ProgramDesc *(&argument->main_program())); + } if (pass_name == "anakin_subgraph_pass") { pass->Set("program", new framework::ProgramDesc *(&argument->main_program())); diff --git a/paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt b/paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt index 05a3d7ddfdb08c98866cc0a08ec4113866c7567d..ddadbc6df4aa3f95b271b011edb85a8d7077796f 100644 --- a/paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt +++ b/paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt @@ -15,7 +15,7 @@ if (WITH_GPU AND TENSORRT_FOUND) set(INFER_IR_PASSES ${INFER_IR_PASSES} tensorrt_subgraph_pass CACHE INTERNAL "") endif() -if (ANAKIN_FOUND) +if (ANAKIN_SUBGRAPH) cc_library(anakin_subgraph_pass SRCS anakin_subgraph_pass.cc DEPS subgraph_detector anakin_op_teller) set(analysis_deps ${analysis_deps} diff --git a/paddle/fluid/inference/analysis/ir_passes/anakin_subgraph_pass.cc b/paddle/fluid/inference/analysis/ir_passes/anakin_subgraph_pass.cc index 9586ce3e6b01422db1616060946cf5b11c5a1c29..a6c6f33cf779f6117d0dda9a9eca279bd846ac84 100644 --- a/paddle/fluid/inference/analysis/ir_passes/anakin_subgraph_pass.cc +++ b/paddle/fluid/inference/analysis/ir_passes/anakin_subgraph_pass.cc @@ -226,7 +226,6 @@ void AnakinSubgraphPass::CreateAnakinEngine( auto max_batch_size = Get("max_batch_size"); auto max_input_shape = Get>>("max_input_shape"); - bool auto_config_layout = Get("auto_config_layout"); if (use_gpu) { #ifdef PADDLE_WITH_CUDA inference::Singleton< @@ -235,11 +234,14 @@ void AnakinSubgraphPass::CreateAnakinEngine( max_input_shape, program_inputs, false, engine_key); #endif } else { +#ifdef ANAKIN_X86_PLACE + bool auto_config_layout = Get("auto_config_layout"); inference::Singleton< anakin::AnakinEngineManager<::anakin::saber::X86, PrecisionT>>::Global() .Create(true, Get("gpu_device_id"), max_batch_size, max_input_shape, program_inputs, auto_config_layout, engine_key); +#endif } auto *scope = param_scope(); @@ -258,6 +260,7 @@ void AnakinSubgraphPass::CreateAnakinEngine( param_set, output_mapping, anakin_engine); #endif } else { +#ifdef ANAKIN_X86_PLACE auto *anakin_engine = inference::Singleton>::Global() @@ -268,6 +271,7 @@ void AnakinSubgraphPass::CreateAnakinEngine( &block_desc_temp, scope, std::vector(input_names.begin(), input_names.end()), param_set, output_mapping, anakin_engine); +#endif } } diff --git a/paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc b/paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc index 76b1671601eec95d64b36effc5727481dcd070e2..670335827b47b9f5308bb7f16620c2b5c07f1c6b 100644 --- a/paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc +++ b/paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc @@ -420,7 +420,7 @@ void SubGraphFuser::ReplaceNodesWithSubGraphs() { // Node that contains this subgraph 2. Mark the nodes inside the sub-graph // as deleted. 3. Replace the deleted node with the new Block Node. framework::OpDesc empty_desc; - empty_desc.SetType("anakin_engine"); + empty_desc.SetType(name_); auto *block_node = graph_->CreateOpNode(&empty_desc); Agent(block_node).set_subgraph({}); auto io = ExtractInputAndOutputOfSubGraph(subgraph); diff --git a/paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h b/paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h index 5d11c217b69f11d45c6fb6d552dc404fa8313daf..26201541f67e3bf8546bc38dbf6823a3dc05a3ee 100644 --- a/paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h +++ b/paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h @@ -18,6 +18,7 @@ limitations under the License. */ #pragma once +#include #include #include "paddle/fluid/framework/ir/graph.h" #include "paddle/fluid/framework/ir/graph_traits.h" @@ -74,10 +75,11 @@ class SubGraphFuser { using NodeInsideSubgraphTeller = SubgraphDetector::NodeInsideSubgraphTeller; SubGraphFuser(Graph *graph, const NodeInsideSubgraphTeller &teller, - int min_subgraph_size) + int min_subgraph_size, std::string name = "anakin_engine") : graph_(graph), node_inside_subgraph_teller_(teller), - min_subgraph_size_{min_subgraph_size} {} + min_subgraph_size_{min_subgraph_size}, + name_{name} {} // The main method which run all the logic. void operator()(); @@ -90,6 +92,7 @@ class SubGraphFuser { Graph *graph_; NodeInsideSubgraphTeller node_inside_subgraph_teller_; int min_subgraph_size_; + const std::string name_; }; struct NodeWrapper { diff --git a/paddle/fluid/inference/analysis/ir_passes/subgraph_util.cc b/paddle/fluid/inference/analysis/ir_passes/subgraph_util.cc index 8f7c6ac7553676b1fb81fea023e50e56ec1d132f..e16cce54c24c2412d3df71e86b23a24329cb61b7 100644 --- a/paddle/fluid/inference/analysis/ir_passes/subgraph_util.cc +++ b/paddle/fluid/inference/analysis/ir_passes/subgraph_util.cc @@ -61,7 +61,7 @@ void RenameAndGetOutputs( std::set *output_names, std::unordered_map *output_name_map, const std::unordered_map &graph_var_map, - bool is_trt) { + bool trt_and_not_int8) { //// In the normal case, the paddle-trt exists bug when runing the googlenet. // When there are more than two convolutions of 1 * 1 with the same input, the // paddle-tensorrt will do the merging optimization, which fuse those conv @@ -121,7 +121,7 @@ void RenameAndGetOutputs( for (auto out_var : correspond_node->outputs) { var2id[out_var->Name()] = out_var->id(); } - if (op_desc.Type() == "conv2d" && is_trt) { + if (op_desc.Type() == "conv2d" && trt_and_not_int8) { auto input_var_name = op_desc.Input("Input").front(); auto filter_var_name = op_desc.Input("Filter").front(); auto out_var_name = op_desc.Output("Output").front(); diff --git a/paddle/fluid/inference/analysis/ir_passes/subgraph_util.h b/paddle/fluid/inference/analysis/ir_passes/subgraph_util.h index bb445027821096689965096c69b8183dd9da403c..444e1984cf8ee52a84e918874e2279b92f78f88e 100644 --- a/paddle/fluid/inference/analysis/ir_passes/subgraph_util.h +++ b/paddle/fluid/inference/analysis/ir_passes/subgraph_util.h @@ -43,7 +43,7 @@ void RenameAndGetOutputs( std::set *output_names, std::unordered_map *output_name_map, const std::unordered_map &graph_var_map, - bool is_trt = true); + bool trt_and_not_int8 = false); } // namespace analysis } // namespace inference diff --git a/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc b/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc index 67650a352d8b8239da228462c21877ff440147b8..37c3fc79554e7ed92877154c6be04b02156ebea0 100644 --- a/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc +++ b/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc @@ -149,6 +149,8 @@ void TensorRtSubgraphPass::CreateTensorRTOp( graph_var_map[node->Name()] = node; } } + auto enable_int8 = Get("enable_int8"); + auto use_calib_mode = Get("use_calib_mode"); auto &subgraph_nodes = *Agent(node).subgraph(); // The following procedure is used to rename all the intermediate @@ -165,7 +167,7 @@ void TensorRtSubgraphPass::CreateTensorRTOp( // it is either an OP's input or an OP's output. RenameAndGetOutputs(subgraph_nodes, &block_desc, input_names_with_id, &output_names_with_id, &output_names, &output_name_map, - graph_var_map); + graph_var_map, !enable_int8); // When tensorrt engine runs at the end of the operation, // output_mapping help us copy the data from the renamed ITensor @@ -196,22 +198,27 @@ void TensorRtSubgraphPass::CreateTensorRTOp( SetAttr(op_desc->Proto(), "output_name_mapping", output_mapping); SetAttr(op_desc->Proto(), "parameters", params); - auto enable_int8 = Get("enable_int8"); auto use_static_engine = Get("use_static_engine"); + // TODO(NHZlX) + // There are models with the same structure but the different parameters, + // when runing in the 'use_serialize' mode, there is a bug. auto engine_key = GenerateEngineKey(input_names_with_id, output_names_with_id, std::to_string(0)); + auto predictor_id = Get("predictor_id"); // Get "" when there is no cached calibration table data. bool load_from_memory = Get("model_from_memory"); std::string calibration_data = ""; - if (enable_int8) { + if (enable_int8 && use_calib_mode) { calibration_data = GetTrtCalibTableData( Get("model_opt_cache_dir"), engine_key, enable_int8); } SetAttr(op_desc->Proto(), "calibration_data", calibration_data); SetAttr(op_desc->Proto(), "enable_int8", enable_int8); + SetAttr(op_desc->Proto(), "use_calib_mode", use_calib_mode); SetAttr(op_desc->Proto(), "engine_key", engine_key); + SetAttr(op_desc->Proto(), "predictor_id", predictor_id); std::string trt_engine_serialized_data = ""; SetAttr(op_desc->Proto(), "engine_serialized_data", trt_engine_serialized_data); @@ -222,7 +229,8 @@ void TensorRtSubgraphPass::CreateTensorRTOp( } // When in int8 mode and calibration_mode, the program just produce the // calibration table data. - bool calibration_mode = (enable_int8 && calibration_data.size() == 0); + bool calibration_mode = + (enable_int8 && calibration_data.size() == 0 && use_calib_mode); if (calibration_mode) { // calibraion mode means generate int8 calibration table data process. return; @@ -230,15 +238,20 @@ void TensorRtSubgraphPass::CreateTensorRTOp( std::copy(params.begin(), params.end(), std::back_inserter(*repetitive_params)); - bool need_serialize = (use_static_engine && !load_from_memory); + tensorrt::TensorRTEngine *trt_engine = + inference::Singleton::Global() + .Create(engine_key + std::to_string(predictor_id), + Get("max_batch_size"), Get("workspace_size"), + enable_int8, calibrator.get(), Get("gpu_device_id")); + + bool need_serialize = (use_static_engine && !load_from_memory); if (need_serialize) { trt_engine_serialized_data = GetTrtEngineSerializedData( Get("model_opt_cache_dir"), engine_key); // we can load the engine info serialized before from the disk. if (!trt_engine_serialized_data.empty()) { - SetAttr(op_desc->Proto(), "engine_serialized_data", - trt_engine_serialized_data); + trt_engine->Deserialize(trt_engine_serialized_data); LOG(INFO) << "Load TRT Optimized Info from " << GetTrtEngineSerializedPath( Get("model_opt_cache_dir"), engine_key); @@ -251,10 +264,7 @@ void TensorRtSubgraphPass::CreateTensorRTOp( // 2. already load serialized trt engine info. LOG(INFO) << "Prepare TRT engine (Optimize model structure, Select OP " "kernel etc). This process may cost a lot of time."; - std::unique_ptr trt_engine( - new tensorrt::TensorRTEngine( - Get("max_batch_size"), Get("workspace_size"), enable_int8, - calibrator.get(), Get("gpu_device_id"))); + auto *scope = param_scope(); framework::BlockDesc block_desc_temp(nullptr, block_desc.Proto()); std::unordered_set param_set(params.begin(), params.end()); @@ -262,20 +272,18 @@ void TensorRtSubgraphPass::CreateTensorRTOp( .ConvertBlockToTRTEngine( &block_desc_temp, *scope, std::vector(input_names.begin(), input_names.end()), - param_set, output_mapping, trt_engine.get()); - nvinfer1::IHostMemory *serialized_engine_data = trt_engine->Serialize(); - trt_engine_serialized_data = - std::string((const char *)serialized_engine_data->data(), - serialized_engine_data->size()); + param_set, output_mapping, trt_engine); if (need_serialize) { + nvinfer1::IHostMemory *serialized_engine_data = trt_engine->Serialize(); + trt_engine_serialized_data = + std::string((const char *)serialized_engine_data->data(), + serialized_engine_data->size()); SaveTrtEngineSerializedDataToFile( GetTrtEngineSerializedPath(Get("model_opt_cache_dir"), engine_key), trt_engine_serialized_data); } - SetAttr(op_desc->Proto(), "engine_serialized_data", - trt_engine_serialized_data); } } // namespace analysis diff --git a/paddle/fluid/inference/analysis/passes/ir_graph_build_pass.cc b/paddle/fluid/inference/analysis/passes/ir_graph_build_pass.cc index c6e923c00484f01f17550ae2926dabcadc0c3ac6..970ecdbbeb0c4c12ce6ba928a74a14ca1ae183ca 100644 --- a/paddle/fluid/inference/analysis/passes/ir_graph_build_pass.cc +++ b/paddle/fluid/inference/analysis/passes/ir_graph_build_pass.cc @@ -13,9 +13,10 @@ // limitations under the License. #include "paddle/fluid/inference/analysis/passes/ir_graph_build_pass.h" -#include +#include #include #include "paddle/fluid/framework/executor.h" +#include "paddle/fluid/framework/ir/fuse_pass_base.h" #include "paddle/fluid/inference/io.h" #include "paddle/fluid/platform/enforce.h" @@ -56,8 +57,9 @@ void IrGraphBuildPass::RunImpl(Argument *argument) { auto graph = std::unique_ptr(new Graph(argument->main_program())); argument->SetMainGraph(graph.release()); - argument->main_graph().Set(framework::ir::kParamScopeAttr, - new framework::Scope *(argument->scope_ptr())); + auto *scope_ptr = argument->scope_ptr(); + PADDLE_ENFORCE(scope_ptr); + argument->main_graph().SetNotOwned(framework::ir::kParamScopeAttr, scope_ptr); } std::unique_ptr IrGraphBuildPass::LoadModel( diff --git a/paddle/fluid/inference/analysis/passes/ir_params_sync_among_devices_pass.cc b/paddle/fluid/inference/analysis/passes/ir_params_sync_among_devices_pass.cc index 1f27e80cf49f49863cf000d71369512242afb7b4..fedee3ff95f0ffe7af730c7113dbe6ea33c118e5 100644 --- a/paddle/fluid/inference/analysis/passes/ir_params_sync_among_devices_pass.cc +++ b/paddle/fluid/inference/analysis/passes/ir_params_sync_among_devices_pass.cc @@ -69,7 +69,7 @@ void IrParamsSyncAmongDevicesPass::RunImpl(Argument *argument) { // Copy the parameter data to a tmp tensor. TensorCopySync(*t, cpu_place, &temp_tensor); // Reallocation the space on GPU - t->mutable_data(place); + t->clear(); // Copy parameter data to newly allocated GPU space. TensorCopySync(temp_tensor, place, t); diff --git a/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc b/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc index 4b0a9d9b1c48fcb0d5e44ec1b977c817f3c70b2e..1f4077eec8f970d72aa15f4bc0f1293e6185fe49 100644 --- a/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc +++ b/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc @@ -15,6 +15,7 @@ #include "paddle/fluid/inference/analysis/passes/memory_optimize_pass.h" #include #include +#include #include #include #include @@ -38,6 +39,14 @@ using framework::ir::Node; using framework::ir::TopologyVarientSort; using space_table_t = MemoryOptimizePass::space_table_t; +typedef struct { + std::string name; + size_t size; + int cluster; + std::pair lifetime; + std::unordered_set adj; +} MemNode; + // Collect the lifecycles of the tensors. // Traverse the graph in topological order. // The traversal order also affect the lifecycles, so different sort_kind is @@ -96,6 +105,89 @@ int DataTypeToSpace(framework::proto::VarType_Type type) { } } +void MemoryOptimizePass::CollectVarMemorySize( + space_table_t* space_table) const { + const int fake_batch_size = 1; + // Collect tensors from graph. + for (auto* node : graph_->Nodes()) { + if (node->IsVar() && + node->Var()->GetType() == + framework::proto::VarType::Type::VarType_Type_LOD_TENSOR) { + // Parameters will not be reused. + if (node->Var()->Persistable()) continue; + auto shape = node->Var()->GetShape(); + for (auto& v : shape) { + if (v < 0) v = fake_batch_size; + } + + int size = std::accumulate(shape.begin(), shape.end(), 1, + std::multiplies()); + (*space_table)[node->Var()->Name()] = + size * DataTypeToSpace(node->Var()->GetDataType()); + } + } +} + +void MakeSimpleReusePlan( + const std::unordered_map>& lifecycles, + const std::unordered_map& space_table, + std::unordered_map* node2cluster, + std::unordered_map* cluster_size) { + std::vector mem_nodes; + for (auto& data : lifecycles) { + MemNode temp_node; + temp_node.name = data.first; + PADDLE_ENFORCE( + space_table.count(data.first), + "%s variable should be in the spacetable during memory optimize", + data.first); + temp_node.size = space_table.at(data.first); + temp_node.cluster = -1; + temp_node.lifetime = data.second; + mem_nodes.push_back(temp_node); + } + auto overlap = [](std::pair a, std::pair b) -> bool { + return b.second >= a.first && a.second >= b.first; + }; + // If the lifetime of two nodes is overwritten, we set them as adjacent nodes. + for (size_t i = 0; i < mem_nodes.size(); i++) { + for (size_t j = i + 1; j < mem_nodes.size(); j++) { + if (overlap(mem_nodes[i].lifetime, mem_nodes[j].lifetime)) { + mem_nodes[i].adj.insert(mem_nodes[j].name); + mem_nodes[j].adj.insert(mem_nodes[i].name); + } + } + } + + // Sort the nodes according to the node memory size. + auto sort_func = [](MemNode a, MemNode b) { return a.size > b.size; }; + std::sort(mem_nodes.begin(), mem_nodes.end(), sort_func); + + // Generating Memory Reuse Strategy Based on Greedy Way + for (size_t i = 0; i < mem_nodes.size(); i++) { + if (mem_nodes[i].cluster >= 0) continue; + int cluster_index = cluster_size->size(); + mem_nodes[i].cluster = cluster_index; + (*cluster_size)[mem_nodes[i].name] = mem_nodes[i].size; + (*node2cluster)[mem_nodes[i].name] = mem_nodes[i].name; + std::unordered_set cluster_adj = mem_nodes[i].adj; + for (size_t j = i + 1; j < mem_nodes.size(); j++) { + if (mem_nodes[j].cluster < 0 && + (cluster_adj.find(mem_nodes[j].name) == cluster_adj.end())) { + (*node2cluster)[mem_nodes[j].name] = mem_nodes[i].name; + mem_nodes[j].cluster = cluster_index; + for (auto& n : mem_nodes[j].adj) { + cluster_adj.insert(n); + } + } + } + } + for (auto& cluster : *cluster_size) { + LOG(INFO) << "Cluster name : " << cluster.first + << " size: " << cluster.second; + } +} + // Collect the memory size of the tensors. void MemoryOptimizePass::CollectVarMemorySize( const std::unordered_map& batch_var_ave_dim, @@ -377,6 +469,17 @@ void UpdateOpDescsByReuse( } } + // modify the graph + for (auto input_node : node->inputs) { + PADDLE_ENFORCE(input_node->IsVar()); + std::string input_node_name = input_node->Name(); + if (reuse_table.count(input_node_name) && + reuse_table.at(input_node_name) != input_node_name) { + auto name = reuse_table.at(input_node_name); + input_node->RenameVar(name); + } + } + for (auto argument : node->Op()->Outputs()) { for (const auto& x : argument.second) { auto name = x; @@ -388,6 +491,17 @@ void UpdateOpDescsByReuse( } } + // modify the graph + for (auto out_node : node->outputs) { + PADDLE_ENFORCE(out_node->IsVar()); + std::string out_node_name = out_node->Name(); + if (reuse_table.count(out_node_name) && + reuse_table.at(out_node_name) != out_node_name) { + auto name = reuse_table.at(out_node_name); + out_node->RenameVar(name); + } + } + // Update arguments. for (auto& arg : in_args) { node->Op()->SetInput(arg.first, arg.second); @@ -589,12 +703,24 @@ void MemoryOptimizePass::RunImpl(Argument* argument) { VLOG(3) << "Load memory cache from " << path; std::vector>> batches; - if (argument->static_memory_optim() && inference::IsFileExists(path)) { + if (!(argument->static_memory_optim() && inference::IsFileExists(path))) { + string::PrettyLogInfo("--- Performing dynamic memory optimize"); + // batches = FakeBatchVarShapes(argument->main_program()); + int sort_kind = 0; + std::unordered_map lifecycles; + space_table_t space_table; + std::unordered_map node2cluster; + std::unordered_map cluster_size; + + CollectLifeCycle(&lifecycles, sort_kind); + CollectVarMemorySize(&space_table); + MakeSimpleReusePlan(lifecycles, space_table, &node2cluster, &cluster_size); + UpdateOpDescsByReuse(graph_, node2cluster, sort_kind); + return; + + } else { string::PrettyLogInfo("--- Performing static memory optimize"); batches = DeseralizeBatchVarShapes(path); - } else { - string::PrettyLogInfo("--- Performing dynamic memory optimize"); - batches = FakeBatchVarShapes(argument->main_program()); } auto var_batch_ave_size = GetBatchAverageSize(batches); diff --git a/paddle/fluid/inference/analysis/passes/memory_optimize_pass.h b/paddle/fluid/inference/analysis/passes/memory_optimize_pass.h index 2da565f2ae15a50a207173b10d4c350456086582..5a907303b4d3ba2d1404de7c5b82527b384aa3de 100644 --- a/paddle/fluid/inference/analysis/passes/memory_optimize_pass.h +++ b/paddle/fluid/inference/analysis/passes/memory_optimize_pass.h @@ -14,6 +14,8 @@ #pragma once #include +#include +#include #include #include #include "paddle/fluid/inference/analysis/analysis_pass.h" @@ -72,6 +74,8 @@ class MemoryOptimizePass : public AnalysisPass { std::unordered_map *lifecycles, int sort_kind) const; + void CollectVarMemorySize(space_table_t *space_table) const; + void CollectVarMemorySize( const std::unordered_map &batch_var_ave_dim, std::unordered_map *tensor_nodes, diff --git a/paddle/fluid/inference/api/CMakeLists.txt b/paddle/fluid/inference/api/CMakeLists.txt index 8b0b76e6539c162d08e811cdd25c14f031da2548..1921e419383a7b3b657bd4b2ce1b04759d472c65 100644 --- a/paddle/fluid/inference/api/CMakeLists.txt +++ b/paddle/fluid/inference/api/CMakeLists.txt @@ -27,20 +27,28 @@ if(WITH_GPU AND TENSORRT_FOUND) set(inference_deps ${inference_deps} tensorrt_engine tensorrt_converter) endif() -if (ANAKIN_FOUND) +if (ANAKIN_SUBGRAPH) set(inference_deps ${inference_deps} anakin_op_converter anakin_engine) endif() +if(WITH_NGRAPH) + set(inference_deps ${inference_deps} ngraph) +endif() + add_subdirectory(details) if(WITH_MKLDNN) - set(mkldnn_quantizer_src mkldnn_quantizer.cc) - set(mkldnn_quantizer_cfg mkldnn_quantizer_config) - cc_library(${mkldnn_quantizer_cfg} SRCS mkldnn_quantizer_config.cc DEPS lod_tensor paddle_pass_builder) + set(mkldnn_quantizer_src mkldnn_quantizer.cc) + set(mkldnn_quantizer_cfg mkldnn_quantizer_config) + cc_library(${mkldnn_quantizer_cfg} SRCS mkldnn_quantizer_config.cc DEPS lod_tensor paddle_pass_builder) endif() cc_library(analysis_config SRCS analysis_config.cc DEPS ${mkldnn_quantizer_cfg} lod_tensor paddle_pass_builder) -cc_library(paddle_pass_builder SRCS paddle_pass_builder.cc) +if(WITH_NGRAPH) + cc_library(paddle_pass_builder SRCS paddle_pass_builder.cc DEPS ngraph) +else(WITH_NGRAPH) + cc_library(paddle_pass_builder SRCS paddle_pass_builder.cc) +endif(WITH_NGRAPH) cc_library(analysis_predictor SRCS analysis_predictor.cc ${mkldnn_quantizer_src} DEPS paddle_inference_api zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder ir_pass_manager ${inference_deps}) cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS @@ -48,9 +56,7 @@ cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS paddle_pass_builder zero_copy_tensor reset_tensor_array) -cc_test(test_paddle_inference_api - SRCS api_tester.cc - DEPS paddle_inference_api) +cc_test(test_paddle_inference_api SRCS api_tester.cc DEPS paddle_inference_api) if(WITH_TESTING) inference_base_test(test_api_impl SRCS api_impl_tester.cc DEPS ${inference_deps} @@ -61,13 +67,21 @@ endif() cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_predictor benchmark ${inference_deps} ARGS --dirname=${WORD2VEC_MODEL_DIR}) -if (WITH_ANAKIN AND WITH_MKL) # only needed in CI - # compile the libinference_anakin_api.a and anakin.so. - cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml zero_copy_tensor_dummy device_context) - cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber zero_copy_tensor_dummy device_context) - function(anakin_target target_name) - target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS}) - endfunction() - anakin_target(inference_anakin_api) - anakin_target(inference_anakin_api_shared) +if(ANAKIN_FOUND) + if (ANAKIN_MLU AND NOT WITH_GPU AND NOT ANAKIN_X86) + message(STATUS "Compile with anakin mlu place.") + add_definitions(-DANAKIN_MLU_PLACE) + elseif(ANAKIN_X86) + message(STATUS "Compile with anakin x86 place.") + add_definitions(-DANAKIN_X86_PLACE) + endif() + cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc) + target_link_libraries(inference_anakin_api anakin anakin_saber_common) + cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc) + target_link_libraries(inference_anakin_api_shared anakin anakin_saber_common) + function(anakin_target target_name) + target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS}) + endfunction() + anakin_target(inference_anakin_api) + anakin_target(inference_anakin_api_shared) endif() diff --git a/paddle/fluid/inference/api/analysis_config.cc b/paddle/fluid/inference/api/analysis_config.cc index 8b940b67e3f9c3e11bb8e15df1a8674bda0c06d0..890c90697bcd52e10560b04981cc50d7b58b2d6e 100644 --- a/paddle/fluid/inference/api/analysis_config.cc +++ b/paddle/fluid/inference/api/analysis_config.cc @@ -21,6 +21,7 @@ #include "paddle/fluid/platform/gpu_info.h" namespace paddle { +extern const std::vector kTRTSubgraphPasses; extern const std::vector kAnakinSubgraphPasses; PassStrategy *AnalysisConfig::pass_builder() const { @@ -86,10 +87,12 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) { // Model related. CP_MEMBER(model_dir_); - CP_MEMBER(prog_file_); - CP_MEMBER(params_file_); CP_MEMBER(model_from_memory_); // the memory model reuses prog_file_ and // params_file_ fields. + + prog_file_ = std::move(other.prog_file_); + params_file_ = std::move(other.params_file_); + // Gpu related. CP_MEMBER(use_gpu_); CP_MEMBER(device_id_); @@ -105,6 +108,9 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) { CP_MEMBER(tensorrt_min_subgraph_size_); CP_MEMBER(tensorrt_precision_mode_); CP_MEMBER(trt_use_static_engine_); + CP_MEMBER(trt_use_calib_mode_); + // NGRAPH related. + CP_MEMBER(use_ngraph_); // MKLDNN related. CP_MEMBER(use_mkldnn_); CP_MEMBER(mkldnn_enabled_op_types_); @@ -168,16 +174,26 @@ void AnalysisConfig::EnableMkldnnQuantizer() { Update(); } -std::shared_ptr AnalysisConfig::mkldnn_quantizer_config() - const { +void AnalysisConfig::EnableNgraph() { +#ifdef PADDLE_WITH_NGRAPH + pass_builder()->EnableNgraph(); + use_ngraph_ = true; +#else + LOG(ERROR) << "Please compile with NGRAPH first to use NGRAPH"; + use_ngraph_ = false; +#endif +} + +MkldnnQuantizerConfig *AnalysisConfig::mkldnn_quantizer_config() const { PADDLE_ENFORCE_NOT_NULL(mkldnn_quantizer_config_, "MkldnnQuantizer was not enabled yet."); - return mkldnn_quantizer_config_; + return mkldnn_quantizer_config_.get(); } void AnalysisConfig::EnableTensorRtEngine( int workspace_size, int max_batch_size, int min_subgraph_size, - AnalysisConfig::Precision precision_mode, bool use_static) { + AnalysisConfig::Precision precision_mode, bool use_static, + bool use_calib_mode) { #ifdef PADDLE_WITH_CUDA if (!use_gpu()) { LOG(ERROR) << "To use TensorRT engine, please call EnableGpu() first"; @@ -190,6 +206,7 @@ void AnalysisConfig::EnableTensorRtEngine( tensorrt_min_subgraph_size_ = min_subgraph_size; tensorrt_precision_mode_ = precision_mode; trt_use_static_engine_ = use_static; + trt_use_calib_mode_ = use_calib_mode; Update(); #else @@ -228,14 +245,24 @@ void AnalysisConfig::Update() { } if (use_tensorrt_) { - const auto &passes = pass_builder_->AllPasses(); - if (std::find(passes.begin(), passes.end(), "tensorrt_subgraph_pass") == - std::end(passes)) { - // Append after the Affine_channel_conv_fuse pass. - pass_builder()->InsertPass(3, "tensorrt_subgraph_pass"); + pass_builder()->ClearPasses(); + for (const auto &pass : kTRTSubgraphPasses) { + pass_builder()->AppendPass(pass); + } + } + + if (use_ngraph_) { + if (!enable_ir_optim_) { + LOG(ERROR) + << "EnableNgraph() only works when IR optimization is enabled."; } - pass_builder()->DeletePass("runtime_context_cache_pass"); - pass_builder()->DeletePass("expected_kernel_cache_pass"); +#ifdef PADDLE_WITH_NGRAPH + pass_builder()->EnableNgraph(); + use_ngraph_ = true; +#else + LOG(ERROR) << "Please compile with NGRAPH first to use NGRAPH"; + use_ngraph_ = false; +#endif } if (use_mkldnn_) { @@ -312,6 +339,8 @@ std::string AnalysisConfig::SerializeInfoCache() { ss << static_memory_optim_; ss << static_memory_optim_force_update_; + ss << use_ngraph_; + ss << use_mkldnn_; for (auto &item : mkldnn_enabled_op_types_) ss << item; ss << ";"; @@ -342,6 +371,7 @@ float AnalysisConfig::fraction_of_gpu_memory_for_pool() const { // Get the GPU memory details and calculate the fraction of memory for the // GPU memory pool. size_t gpu_used, gpu_available; + platform::SetDeviceId(device_id_); platform::GpuMemoryUsage(&gpu_used, &gpu_available); double total_gpu_memory = (gpu_used + gpu_available) / 1024. / 1024.; float fraction_of_gpu_memory = @@ -412,4 +442,12 @@ void AnalysisConfig::EnableAnakinEngine( anakin_auto_config_layout_ = auto_config_layout; Update(); } + +void AnalysisConfig::PartiallyRelease() { + prog_file_.clear(); + prog_file_.shrink_to_fit(); + params_file_.clear(); + params_file_.shrink_to_fit(); +} + } // namespace paddle diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index e57d3a80456767848143412b2524f94fa09c7c13..5d9d5a3178aaa39f4b80197fb5ac7cd46504bf4f 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -202,6 +202,7 @@ bool AnalysisPredictor::Run(const std::vector &inputs, timer.tic(); // set feed variable framework::Scope *scope = sub_scope_ ? sub_scope_ : scope_.get(); + PADDLE_ENFORCE_NOT_NULL(scope, "The scope should not be nullptr."); if (!SetFeed(inputs, scope)) { LOG(ERROR) << "fail to set feed"; return false; @@ -229,8 +230,15 @@ bool AnalysisPredictor::Run(const std::vector &inputs, // Here is a bugfix, collect all the container variables, and reset then to a // bool; the next time, the operator will call MutableData and construct a new // container again, so that the container will be empty for each batch. - tensor_array_batch_cleaner_.CollectNoTensorVars(sub_scope_); + if (sub_scope_) { + tensor_array_batch_cleaner_.CollectNoTensorVars(sub_scope_); + } tensor_array_batch_cleaner_.ResetNoTensorVars(); + + // recover the cpu_math_library_num_threads to 1, in order to avoid thread + // conflict when integrating it into deployment service. + paddle::platform::SetNumThreads(1); + return true; } @@ -385,6 +393,7 @@ void AnalysisPredictor::PrepareArgument() { argument_.SetTensorRtMinSubgraphSize(config_.tensorrt_min_subgraph_size_); argument_.SetTensorRtPrecisionMode(config_.tensorrt_precision_mode_); argument_.SetTensorRtUseStaticEngine(config_.trt_use_static_engine_); + argument_.SetTensorRtUseCalibMode(config_.trt_use_calib_mode_); } if (config_.anakin_engine_enabled()) { @@ -435,6 +444,10 @@ void AnalysisPredictor::OptimizeInferenceProgram() { ARGUMENT_CHECK_FIELD((&argument_), ir_analyzed_program); inference_program_.reset( new framework::ProgramDesc(argument_.ir_analyzed_program())); + // The config and argument take a lot of storage, + // when the predictor settings are complete, we release these stores. + argument_.PartiallyRelease(); + config_.PartiallyRelease(); LOG(INFO) << "== optimize end =="; } @@ -442,6 +455,8 @@ template <> std::unique_ptr CreatePaddlePredictor< AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig &config) { VLOG(3) << "create AnalysisConfig"; + PADDLE_ENFORCE(config.is_valid(), + "Note: Each config can only be used for one predictor."); if (config.use_gpu()) { // 1. GPU memory PADDLE_ENFORCE_GE(config.memory_pool_init_size_mb(), 0.f); @@ -471,6 +486,8 @@ std::unique_ptr CreatePaddlePredictor< } std::unique_ptr predictor(new AnalysisPredictor(config)); + // Each config can only be used for one predictor. + config.SetInValid(); auto predictor_p = dynamic_cast(predictor.get()); if (!predictor_p->Init(nullptr)) { @@ -582,6 +599,11 @@ bool AnalysisPredictor::ZeroCopyRun() { // Fix TensorArray reuse not cleaned bug. tensor_array_batch_cleaner_.CollectTensorArrays(sub_scope_); tensor_array_batch_cleaner_.ResetTensorArray(); + + // recover the cpu_math_library_num_threads to 1, in order to avoid thread + // conflict when integrating it into deployment service. + paddle::platform::SetNumThreads(1); + return true; } diff --git a/paddle/fluid/inference/api/analysis_predictor_tester.cc b/paddle/fluid/inference/api/analysis_predictor_tester.cc index 6bc892638c28ca0b5bab82936bf9700289bed6b2..44b1b8071de9d0e825ea4c8ee895c44b8951f14f 100644 --- a/paddle/fluid/inference/api/analysis_predictor_tester.cc +++ b/paddle/fluid/inference/api/analysis_predictor_tester.cc @@ -260,7 +260,7 @@ class MkldnnQuantizerTest : public testing::Test { predictor.reset(new AnalysisPredictor(config)); auto* predictor_p = static_cast(predictor.get()); - auto qconfig = std::make_shared(); + auto qconfig = new MkldnnQuantizerConfig(); mkldnn_quantizer.reset( new AnalysisPredictor::MkldnnQuantizer(*predictor_p, qconfig)); @@ -384,7 +384,7 @@ TEST_F(MkldnnQuantizerTest, histogram_empty) { // zero tensor framework::LoDTensor var_tensor; var_tensor.Resize({0}); - ASSERT_TRUE(var_tensor.mutable_data(platform::CPUPlace())); + var_tensor.mutable_data(platform::CPUPlace()); ASSERT_THROW(Histogram(var_tensor, -1, 1, 1), platform::EnforceNotMet); } diff --git a/paddle/fluid/inference/api/api_anakin_engine.cc b/paddle/fluid/inference/api/api_anakin_engine.cc index 2c4894fd887f2f509dc7ab88c367cea5c1aed99a..63d23321ab41eb9f26a4cd79f3ffa799e3795e72 100644 --- a/paddle/fluid/inference/api/api_anakin_engine.cc +++ b/paddle/fluid/inference/api/api_anakin_engine.cc @@ -1,4 +1,4 @@ -// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -12,19 +12,14 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "paddle/fluid/inference/api/api_anakin_engine.h" - -#ifdef PADDLE_WITH_CUDA -#include -#endif - -#include -#include #include #include #include #include +#include "paddle/fluid/inference/api/api_anakin_engine.h" +#include "paddle/fluid/inference/api/paddle_api.h" + #include "framework/core/net/net.h" #include "framework/operators/ops.h" #include "saber/funcs/timer.h" @@ -32,209 +27,346 @@ namespace paddle { using paddle::contrib::AnakinConfig; +template +extern std::mutex PaddleInferenceAnakinPredictor::mutex_; +template +extern std::once_flag PaddleInferenceAnakinPredictor::init_anakin_; -template -PaddleInferenceAnakinPredictor::PaddleInferenceAnakinPredictor( - const contrib::AnakinConfig &config) { - CHECK(Init(config)); +template +void PaddleInferenceAnakinPredictor::InitEnv() { + anakin::TargetWrapper::set_device(this->config_.device_id); + std::call_once(this->init_anakin_, [this]() { + anakin::Env::env_init(this->config_.max_stream); + }); } -template <> -PaddleInferenceAnakinPredictor::PaddleInferenceAnakinPredictor( - const contrib::AnakinConfig &config) { - omp_set_dynamic(0); - omp_set_num_threads(1); - mkl_set_num_threads(1); - CHECK(Init(config)); +template +void PaddleInferenceAnakinPredictor::InitNet() { + std::unique_lock lock(this->mutex_); + this->executor_p_ = new anakin::Net(*this->graph_p_, true); } -template -bool PaddleInferenceAnakinPredictor::Init( - const contrib::AnakinConfig &config) { - if (!(graph_.load(config.model_file))) { - VLOG(3) << "fail to load graph from " << config.model_file; - return false; +template +void PaddleInferenceAnakinPredictor::SetContext() { + this->ctx_p_ = std::make_shared>( + this->config_.device_id, this->config_.data_stream_id, + this->config_.compute_stream_id); +} +template +void PaddleInferenceAnakinPredictor::InitGraph() { + this->graph_p_ = + std::make_shared>(); + if (!(this->graph_p_->load(this->config_.model_file))) { + LOG(FATAL) << "fail to load graph from " << this->config_.model_file; } - auto inputs = graph_.get_ins(); + auto inputs = this->graph_p_->get_ins(); for (auto &input_str : inputs) { - graph_.ResetBatchSize(input_str, config.max_batch_size); - max_batch_size_ = config.max_batch_size; + if (this->config_.init_inputs_shape.find(input_str) == + this->config_.init_inputs_shape.end()) { + LOG(FATAL) << input_str << " is not implemented."; + } + std::vector shape = + this->config_.init_inputs_shape.find(input_str)->second; + this->graph_p_->Reshape(input_str, shape); } - // optimization for graph - if (!(graph_.Optimize())) { - return false; +} +template +void PaddleInferenceAnakinPredictor::OptimizeGraph() { + if (!this->graph_p_->Optimize()) { + LOG(FATAL) << "Graph optimization error."; } - // construct executer - if (executor_p_ == nullptr) { - executor_p_ = new anakin::Net(graph_, true); +} +template +void PaddleInferenceAnakinPredictor::InitPredictor() { + this->InitEnv(); + this->SetContext(); + this->InitGraph(); + this->OptimizeGraph(); + this->InitNet(); +} +template +void PaddleInferenceAnakinPredictor::Predict() { + anakin::TargetWrapper::device_sync(); + this->executor_p_->prediction(); + anakin::TargetWrapper::device_sync(); +} +template +bool PaddleInferenceAnakinPredictor::Run( + const std::vector &inputs, + std::vector *output_data, int batch_size) { + if (this->config_.re_allocable) { + return this->RunImpl(inputs, output_data); + } else { + // Run inputs data that exceeds batch size in batches. + // 1. Reassign the batch size. + if (batch_size == -1) { + if (!inputs[0].lod.empty()) { + batch_size = inputs[0].lod[0].size() - 1; + } else { + batch_size = inputs[0].shape[0]; + } + } + // 2. If the data don't need to be batched, run it directly. + if (batch_size <= this->config_.init_batch_size) { + return this->RunImpl(inputs, output_data); + } + // 3. Check the batch size and define temporary variables. + std::vector cur_inputs; + std::vector outputs_master; + std::vector> outputs_vec; + for (const auto &input : inputs) { + if (!input.lod.empty()) { + if (input.lod.size() != 1) { + return false; + } + if (input.lod[0].size() - 1 != batch_size) { + return false; + } + } else { + LOG(INFO) << "Non-lod mode to be implemented."; + return false; + } + PaddleTensor tensor; + tensor.name = input.name; + tensor.dtype = PaddleDType::FLOAT32; + cur_inputs.push_back(tensor); + } + for (auto output : *output_data) { + PaddleTensor tensor; + tensor.name = output.name; + outputs_master.push_back(tensor); + } + // 4. Batch execution. + for (size_t start_batch = 0; start_batch < batch_size;) { + auto end_batch = start_batch + this->config_.init_batch_size; + if (end_batch > batch_size) { + end_batch = batch_size; + } + auto cur_outputs = outputs_master; + for (size_t i = 0; i < inputs.size(); i++) { + auto start = inputs[i].lod[0][start_batch]; + auto end = inputs[i].lod[0][end_batch]; + std::vector offsets; + for (size_t j = start_batch; j <= end_batch; j++) { + offsets.push_back(inputs[i].lod[0][j] - + inputs[i].lod[0][start_batch]); + } + auto mem_start = static_cast(inputs[i].data.data()) + start; + cur_inputs[i].data = + PaddleBuf(mem_start, (end - start) * sizeof(float)); + cur_inputs[i].lod = std::vector>({offsets}); + cur_inputs[i].shape = + std::vector({static_cast(end - start), 1, 1, 1}); + } + if (!this->RunImpl(cur_inputs, &cur_outputs)) { + return false; + } + outputs_vec.push_back(cur_outputs); + start_batch = end_batch; + } + // 5. Copy the results to contiguous memory. + // Assume that each batch has the same final outputs size. + auto count = [](const std::vector &v) { + int cnt = 1; + for_each(v.begin(), v.end(), [&cnt](int n) { cnt *= n; }); + return cnt; + }; + for (size_t i = 0; i < output_data->size(); i++) { + std::vector shape = outputs_vec[i][0].shape; + shape[0] = batch_size; + int total_cnt = count(shape); + (*output_data)[i].shape = shape; + (*output_data)[i].data.Resize(total_cnt * sizeof(float)); + float *addr = static_cast((*output_data)[i].data.data()); + for (const auto &single_out : outputs_vec) { + int cnt = count(single_out[i].shape); + memcpy(addr, single_out[i].data.data(), cnt * sizeof(float)); + addr += cnt; + } + } } return true; } - -template -bool PaddleInferenceAnakinPredictor::Run( +template +bool PaddleInferenceAnakinPredictor::RunImpl( const std::vector &inputs, - std::vector *output_data, int batch_size) { + std::vector *output_data) { for (const auto &input : inputs) { if (input.dtype != PaddleDType::FLOAT32) { - VLOG(3) << "Only support float type inputs. " << input.name - << "'s type is not float"; - return false; + LOG(FATAL) << "Only support float type inputs. " << input.name + << "'s type is not float"; } - auto d_tensor_in_p = executor_p_->get_in(input.name); - auto net_shape = d_tensor_in_p->shape(); + auto d_tensor_p = this->executor_p_->get_in(input.name); + auto net_shape = d_tensor_p->shape(); if (net_shape.size() != input.shape.size()) { - VLOG(3) << " input " << input.name - << "'s shape size should be equal to that of net"; - return false; + LOG(FATAL) << " input " << input.name + << "'s shape size should be equal to that of net"; } int sum = 1; for_each(input.shape.begin(), input.shape.end(), [&](int n) { sum *= n; }); if (sum > net_shape.count()) { - graph_.Reshape(input.name, input.shape); - delete executor_p_; - executor_p_ = new anakin::Net(graph_, true); - d_tensor_in_p = executor_p_->get_in(input.name); + if (this->config_.re_allocable) { + this->graph_p_->Reshape(input.name, input.shape); + delete this->executor_p_; + this->InitNet(); + d_tensor_p = this->executor_p_->get_in(input.name); + } else { + LOG(FATAL) + << "Run failed because Anakin was expected not to reallocate " + "memory."; + } } - - anakin::saber::Shape tmp_shape; + std::vector tmp_shape; for (auto s : input.shape) { tmp_shape.push_back(s); } - d_tensor_in_p->reshape(tmp_shape); + auto *data = static_cast(input.data.data()); + anakin::saber::Tensor::Host_type> + h_tensor(data, typename anakin::DefaultHostType::Host_type(), 0, + tmp_shape); + d_tensor_p->reshape(tmp_shape); if (input.lod.size() > 0) { if (input.lod.size() > 1) { - VLOG(3) << " input lod first dim should <=1, but you set " - << input.lod.size(); - return false; + LOG(FATAL) << " input lod first dim should <=1, but you set " + << input.lod.size(); } - std::vector offset(input.lod[0].begin(), input.lod[0].end()); - d_tensor_in_p->set_seq_offset(offset); - VLOG(3) << "offset.size(): " << offset.size(); - for (int i = 0; i < offset.size(); i++) { - VLOG(3) << offset[i]; - } - } - - float *d_data_p = d_tensor_in_p->mutable_data(); - -#ifdef PADDLE_WITH_CUDA - if (std::is_same::value) { - if (cudaMemcpy(d_data_p, static_cast(input.data.data()), - d_tensor_in_p->valid_size() * sizeof(float), - cudaMemcpyHostToDevice) != 0) { - VLOG(3) << "copy data from CPU to GPU error"; - return false; + std::vector lod(input.lod[0].begin(), input.lod[0].end()); + std::vector> offset({lod}); + d_tensor_p->set_seq_offset(offset); + VLOG(3) << "offset.size(): " << offset[0].size(); + for (int i = 0; i < offset[0].size(); i++) { + VLOG(3) << offset[0][i]; } } -#endif - if (std::is_same::value) { - memcpy(d_data_p, static_cast(input.data.data()), - d_tensor_in_p->valid_size() * sizeof(float)); - } + d_tensor_p->copy_from(h_tensor); } -#ifdef PADDLE_WITH_CUDA - cudaDeviceSynchronize(); - executor_p_->prediction(); - cudaDeviceSynchronize(); -#endif - + this->Predict(); if (output_data->empty()) { - VLOG(3) << "At least one output should be set with tensors' names."; - return false; + LOG(FATAL) << "At least one output should be set with tensors' names."; } for (auto &output : *output_data) { - auto *tensor = executor_p_->get_out(output.name); - output.shape = tensor->valid_shape(); - if (output.data.length() < tensor->valid_size() * sizeof(float)) { - output.data.Resize(tensor->valid_size() * sizeof(float)); - } - -#if PADDLE_WITH_CUDA - if (std::is_same::value) { - // Copy data from GPU -> CPU - if (cudaMemcpy(output.data.data(), tensor->mutable_data(), - tensor->valid_size() * sizeof(float), - cudaMemcpyDeviceToHost) != 0) { - VLOG(3) << "copy data from GPU to CPU error"; - return false; - } - } -#endif - if (std::is_same::value) { - memcpy(output.data.data(), tensor->mutable_data(), - tensor->valid_size() * sizeof(float)); + auto *d_tensor_p = this->executor_p_->get_out(output.name); + output.shape = d_tensor_p->valid_shape(); + if (output.data.length() < d_tensor_p->valid_size() * sizeof(float)) { + output.data.Resize(d_tensor_p->valid_size() * sizeof(float)); } + auto *data = static_cast(output.data.data()); + anakin::saber::Tensor::Host_type> + h_tensor(data, typename anakin::DefaultHostType::Host_type(), 0, + d_tensor_p->valid_shape()); + h_tensor.copy_from(*d_tensor_p); } return true; } - -template -anakin::Net - &PaddleInferenceAnakinPredictor::get_executer() { - return *executor_p_; +template +bool PaddleInferenceAnakinPredictor::ResetConfig( + const AnakinConfig &config) { + this->config_ = config; + return true; +} +template +anakin::Net &PaddleInferenceAnakinPredictor::ResetExecuter( + std::shared_ptr> graph_p) { + this->graph_p_ = graph_p; + this->ctx_p_ = std::make_shared>( + this->config_.device_id, this->config_.data_stream_id, + this->config_.compute_stream_id); + this->InitNet(); + return *this->executor_p_; } - // the cloned new Predictor of anakin share the same net weights from original // Predictor -template +template std::unique_ptr -PaddleInferenceAnakinPredictor::Clone() { +PaddleInferenceAnakinPredictor::Clone() { VLOG(3) << "Anakin Predictor::clone"; std::unique_ptr cls( - new PaddleInferenceAnakinPredictor()); + new PaddleInferenceAnakinPredictor()); // construct executer from other graph auto anakin_predictor_p = - dynamic_cast *>(cls.get()); + dynamic_cast *>(cls.get()); if (!anakin_predictor_p) { - VLOG(3) << "fail to call Init"; - return nullptr; + LOG(FATAL) << "fail to call Init"; } - anakin_predictor_p->get_executer().init(graph_); + anakin_predictor_p->ResetConfig(this->config_); + anakin_predictor_p->ResetExecuter(this->graph_p_); + return cls; +} - return std::move(cls); +#ifdef ANAKIN_MLU_PLACE +template +void PaddleInferenceAnakinMLUPredictor::SetContext() { + this->ctx_p_ = std::make_shared>( + this->config_.device_id, this->config_.data_stream_id, + this->config_.compute_stream_id); + this->ctx_p_->set_model_parallel(this->config_.model_parallel); + this->ctx_p_->set_fusion(this->config_.op_fuse); } +template +void PaddleInferenceAnakinMLUPredictor::OptimizeGraph() { + if (!this->graph_p_->fusion_optimize(this->config_.op_fuse)) { + LOG(FATAL) << "Graph optimization error."; + } +} +template +void PaddleInferenceAnakinMLUPredictor::InitNet() { + std::unique_lock lock(this->mutex_); + this->executor_p_ = new anakin::Net(); + this->executor_p_->fusion_init(*this->graph_p_, this->ctx_p_, true); +} +template +void PaddleInferenceAnakinMLUPredictor::Predict() { + anakin::TargetWrapper::device_sync(); + this->executor_p_->fusion_prediction(); + anakin::TargetWrapper::device_sync(); +} +#endif #ifdef PADDLE_WITH_CUDA -template class PaddleInferenceAnakinPredictor; +template class PaddleInferenceAnakinPredictor< + anakin::NV, anakin::Precision::FP32, ::anakin::OpRunType::ASYNC>; +#endif +#ifdef ANAKIN_X86_PLACE +template class PaddleInferenceAnakinPredictor< + anakin::X86, anakin::Precision::FP32, ::anakin::OpRunType::ASYNC>; +#endif +#ifdef ANAKIN_MLU_PLACE +template class PaddleInferenceAnakinMLUPredictor; #endif -template class PaddleInferenceAnakinPredictor; // A factory to help create difference predictor. template <> std::unique_ptr CreatePaddlePredictor( const contrib::AnakinConfig &config) { - VLOG(3) << "Anakin Predictor create."; - if (config.target_type == contrib::AnakinConfig::NVGPU) { #ifdef PADDLE_WITH_CUDA - VLOG(3) << "Anakin Predictor create on [ NVIDIA GPU ]."; - std::unique_ptr x( - new PaddleInferenceAnakinPredictor(config)); - return x; -#else - LOG(ERROR) << "AnakinConfig::NVGPU could not used in ONLY-CPU environment"; - return nullptr; + if (config.target_type == contrib::AnakinConfig::NVGPU) { + return std::unique_ptr( + new PaddleInferenceAnakinPredictor(config)); + } #endif - } else if (config.target_type == contrib::AnakinConfig::X86) { - VLOG(3) << "Anakin Predictor create on [ Intel X86 ]."; - std::unique_ptr x( - new PaddleInferenceAnakinPredictor(config)); - return x; - } else { - VLOG(3) << "Anakin Predictor create on unknown platform."; - return nullptr; +#ifdef ANAKIN_X86_PLACE + if (config.target_type == contrib::AnakinConfig::X86) { + return std::unique_ptr( + new PaddleInferenceAnakinPredictor(config)); } +#endif +#ifdef ANAKIN_MLU_PLACE + if (config.target_type == contrib::AnakinConfig::MLU) { + return std::unique_ptr( + new PaddleInferenceAnakinMLUPredictor( + config)); + } +#endif + LOG(FATAL) << "Anakin Predictor create on unknown platform."; + return nullptr; } - +template +void DisplayOpTimer(anakin::Net *net_executor, int epoch) { #ifdef PADDLE_ANAKIN_ENABLE_OP_TIMER -template -using executor_t = - anakin::Net; - -template -void DisplayOpTimer(executor_t *net_executor, int epoch) { std::vector op_time = net_executor->get_op_time(); auto exec_funcs = net_executor->get_exec_funcs(); auto op_param = net_executor->get_op_param(); @@ -254,16 +386,13 @@ void DisplayOpTimer(executor_t *net_executor, int epoch) { for (auto it = op_map.begin(); it != op_map.end(); ++it) { LOG(INFO) << it->first << " " << (it->second) / epoch << " ms"; } -} #endif - -template -PaddleInferenceAnakinPredictor::~PaddleInferenceAnakinPredictor() { -#ifdef PADDLE_ANAKIN_ENABLE_OP_TIMER - DisplayOpTimer(executor_p_, max_batch_size_); -#endif - delete executor_p_; - executor_p_ = nullptr; +} +template +PaddleInferenceAnakinPredictor::~PaddleInferenceAnakinPredictor() { + DisplayOpTimer(this->executor_p_, this->config_.init_batch_size); + delete this->executor_p_; + this->executor_p_ = nullptr; } } // namespace paddle diff --git a/paddle/fluid/inference/api/api_anakin_engine.h b/paddle/fluid/inference/api/api_anakin_engine.h index e14d93de2c41f740bc175c8e59412d7b828dd381..0f0d7febe2ed7331830227c95fed31ac496fa129 100644 --- a/paddle/fluid/inference/api/api_anakin_engine.h +++ b/paddle/fluid/inference/api/api_anakin_engine.h @@ -1,4 +1,4 @@ -/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -19,6 +19,7 @@ limitations under the License. */ #pragma once +#include #include #include "framework/core/net/net.h" @@ -30,13 +31,18 @@ limitations under the License. */ namespace paddle { using contrib::AnakinConfig; +using anakin::Precision; +using anakin::OpRunType; -template +template class PaddleInferenceAnakinPredictor : public PaddlePredictor { public: - PaddleInferenceAnakinPredictor() {} + PaddleInferenceAnakinPredictor() = default; - explicit PaddleInferenceAnakinPredictor(const AnakinConfig& config); + explicit PaddleInferenceAnakinPredictor(const AnakinConfig& config) + : config_(config) { + this->InitPredictor(); + } // NOTE Unlike the native engine, the buffers of anakin engine's output_data // should be allocated first. @@ -45,21 +51,45 @@ class PaddleInferenceAnakinPredictor : public PaddlePredictor { int batch_size = -1) override; std::unique_ptr Clone() override; - - anakin::Net& - get_executer(); + virtual bool ResetConfig(const AnakinConfig& config); + virtual anakin::Net& ResetExecuter( + std::shared_ptr> graph_p); + void InitPredictor(); ~PaddleInferenceAnakinPredictor() override; - private: - bool Init(const AnakinConfig& config); - - anakin::graph::Graph - graph_; - anakin::Net* - executor_p_{nullptr}; + protected: + void InitEnv(); + void InitGraph(); + virtual void OptimizeGraph(); + virtual void InitNet(); + virtual void SetContext(); + virtual void Predict(); + static std::mutex mutex_; AnakinConfig config_; - int max_batch_size_{0}; + std::shared_ptr> ctx_p_; + std::shared_ptr> graph_p_; + anakin::Net* executor_p_{nullptr}; + + private: + bool RunImpl(const std::vector& inputs, + std::vector* output_data); + static std::once_flag init_anakin_; }; +#ifdef ANAKIN_MLU_PLACE +template +class PaddleInferenceAnakinMLUPredictor final + : public PaddleInferenceAnakinPredictor { + public: + explicit PaddleInferenceAnakinMLUPredictor(const AnakinConfig& config) { + this->ResetConfig(config); + this->InitPredictor(); + } + void SetContext() override; + void OptimizeGraph() override; + void InitNet() override; + void Predict() override; +}; +#endif } // namespace paddle diff --git a/paddle/fluid/inference/api/demo_ci/CMakeLists.txt b/paddle/fluid/inference/api/demo_ci/CMakeLists.txt index 19ef402d6fd78d6a65bdb0bbd22198f36b872a27..8c4ce84fa61a9b7c0f409a4adac4abb7e978f5c5 100644 --- a/paddle/fluid/inference/api/demo_ci/CMakeLists.txt +++ b/paddle/fluid/inference/api/demo_ci/CMakeLists.txt @@ -53,10 +53,8 @@ if (WIN32) safe_set_static_flag() add_definitions(-DSTATIC_LIB) endif() - set(CMAKE_STATIC_LIBRARY_PREFIX "lib") else() set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") - set(CMAKE_STATIC_LIBRARY_PREFIX "") endif() message("flags" ${CMAKE_CXX_FLAGS}) @@ -125,11 +123,8 @@ if (NOT WIN32) else() set(DEPS ${DEPS} ${MATH_LIB} ${MKLDNN_LIB} - ${CMAKE_STATIC_LIBRARY_PREFIX}glog ${CMAKE_STATIC_LIBRARY_PREFIX}gflags ${CMAKE_STATIC_LIBRARY_PREFIX}protobuf - ${CMAKE_STATIC_LIBRARY_PREFIX}snappy ${CMAKE_STATIC_LIBRARY_PREFIX}z ${CMAKE_STATIC_LIBRARY_PREFIX}xxhash - snappystream ${EXTERNAL_LIB}) - get_property(os_dependency_modules GLOBAL PROPERTY OS_DEPENDENCY_MODULES) - set(DEPS ${DEPS} libcmt ${os_dependency_modules}) + glog gflags_static protobuf snappy zlibstatic xxhash snappystream ${EXTERNAL_LIB}) + set(DEPS ${DEPS} libcmt shlwapi.lib) endif(NOT WIN32) if(WITH_GPU) diff --git a/paddle/fluid/inference/api/helper.h b/paddle/fluid/inference/api/helper.h index ab7f55337488f9e4c953210124e47c12e26ed6b1..e5820c3637bcafd7bcf1e530770748486490045a 100644 --- a/paddle/fluid/inference/api/helper.h +++ b/paddle/fluid/inference/api/helper.h @@ -21,6 +21,7 @@ #endif #include #include // NOLINT +#include #include #include #include @@ -63,9 +64,12 @@ static int GetUniqueId() { } static void split(const std::string &str, char sep, - std::vector *pieces) { + std::vector *pieces, bool ignore_null = true) { pieces->clear(); if (str.empty()) { + if (!ignore_null) { + pieces->push_back(str); + } return; } size_t pos = 0; @@ -79,26 +83,63 @@ static void split(const std::string &str, char sep, pieces->push_back(str.substr(pos)); } } + +template +static T convert(const std::string &item, + std::function func) { + T res; + try { + res = func(item); + } catch (std::invalid_argument &e) { + std::string message = + "invalid_argument exception when try to convert : " + item; + LOG(ERROR) << message; + PADDLE_THROW(message); + } catch (std::out_of_range &e) { + std::string message = + "out_of_range exception when try to convert : " + item; + LOG(ERROR) << message; + PADDLE_THROW(message); + } catch (...) { + std::string message = "unexpected exception when try to convert " + item; + LOG(ERROR) << message; + PADDLE_THROW(message); + } + return res; +} + static void split_to_float(const std::string &str, char sep, std::vector *fs) { std::vector pieces; split(str, sep, &pieces); std::transform(pieces.begin(), pieces.end(), std::back_inserter(*fs), - [](const std::string &v) { return std::stof(v); }); + [](const std::string &v) { + return convert(v, [](const std::string &item) { + return std::stof(item); + }); + }); } static void split_to_int64(const std::string &str, char sep, std::vector *is) { std::vector pieces; split(str, sep, &pieces); std::transform(pieces.begin(), pieces.end(), std::back_inserter(*is), - [](const std::string &v) { return std::stoi(v); }); + [](const std::string &v) { + return convert(v, [](const std::string &item) { + return std::stoll(item); + }); + }); } static void split_to_int(const std::string &str, char sep, std::vector *is) { std::vector pieces; split(str, sep, &pieces); std::transform(pieces.begin(), pieces.end(), std::back_inserter(*is), - [](const std::string &v) { return std::stoi(v); }); + [](const std::string &v) { + return convert(v, [](const std::string &item) { + return std::stoi(item); + }); + }); } template std::string to_string(const std::vector &vec) { diff --git a/paddle/fluid/inference/api/mkldnn_quantizer.cc b/paddle/fluid/inference/api/mkldnn_quantizer.cc index de75e884f53143d9026636ad8663d89a36a30f69..9d560ddd2e039cfa01c01ae159cb9f7b95cb638a 100644 --- a/paddle/fluid/inference/api/mkldnn_quantizer.cc +++ b/paddle/fluid/inference/api/mkldnn_quantizer.cc @@ -50,40 +50,48 @@ bool AnalysisPredictor::MkldnnQuantizer::CalculateScales() { auto glambda = [&](const VariableNameMap& connections, bool is_output) { for (auto const& conn : connections) { - if (conn.second.size() == 0) continue; - auto& var_name = conn.second[0]; - - // skip if scale already computed - if (scales_.find(var_name) != scales_.end()) return; - - auto* var = predictor_.sub_scope_->FindVar(var_name); - PADDLE_ENFORCE(var, "%s is not in the scope", var_name); - PADDLE_ENFORCE(var->IsType(), - "Only support lod tensor now."); - LoDTensor* var_tensor = var->GetMutable(); - - // force unsigned type if already know it - bool is_unsigned = false; - if (is_output && op->Type() == "conv2d") { - // output of conv2d with relu must be unsigned - is_unsigned = op->HasAttr("fuse_relu") && - boost::get(op->GetAttr("fuse_relu")); - } else if (is_output && op->Type() == "pool2d") { - // output of pool2d with unsigned input must be unsigned - auto input_var_name = op->Input("X")[0]; - if (scales_.find(input_var_name) != scales_.end()) { - is_unsigned = scales_[input_var_name].first; + for (const auto& var_name : conn.second) { + // skip if scale already computed + if (scales_.find(var_name) != scales_.end()) return; + + auto* var = predictor_.sub_scope_->FindVar(var_name); + PADDLE_ENFORCE(var, "%s is not in the scope", var_name); + PADDLE_ENFORCE(var->IsType(), + "Only support lod tensor now."); + LoDTensor* var_tensor = var->GetMutable(); + + // force unsigned type if already know it + bool is_unsigned = false; + if (is_output && op->Type() == "conv2d") { + // output of conv2d with relu must be unsigned + is_unsigned = (op->HasAttr("fuse_relu") && + boost::get(op->GetAttr("fuse_relu"))) || + (op->HasAttr("fuse_brelu") && + boost::get(op->GetAttr("fuse_brelu"))); + } else if (is_output && op->Type() == "relu") { + is_unsigned = true; + } else if (is_output && + (op->Type() == "pool2d" || op->Type() == "transpose2" || + op->Type() == "reshape2" || op->Type() == "concat")) { + // output of ops with unsigned input must be unsigned + is_unsigned = true; + for (auto input_var_name : op->Input("X")) { + PADDLE_ENFORCE(scales_.find(input_var_name) != scales_.end(), + "Input scales must be calculated before the " + "output scales to infer if output is unsigned."); + is_unsigned = is_unsigned && scales_[input_var_name].first; + } } - } - CalculateSingleScale(op->Type(), conn.first, var_name, *var_tensor, - is_unsigned); + CalculateSingleScale(op->Type(), conn.first, var_name, *var_tensor, + is_unsigned); + } } }; - // handle outputs first so unsigned outputs could be inferred - glambda(connections_out, true /* is_output */); + // handle inputs first to let is_unsigned be inferred for the outputs glambda(connections_in, false /* is_output */); + glambda(connections_out, true /* is_output */); } } @@ -353,8 +361,9 @@ void AnalysisPredictor::MkldnnQuantizer::PrepareArgument() const { arg.SetMainProgramNotOwned(predictor_.inference_program_.get()); auto graph = std::unique_ptr(new Graph(arg.main_program())); arg.SetMainGraph(graph.release()); - arg.main_graph().Set(framework::ir::kParamScopeAttr, - new framework::Scope*(arg.scope_ptr())); + auto* scope_ptr = arg.scope_ptr(); + PADDLE_ENFORCE(scope_ptr); + arg.main_graph().SetNotOwned(framework::ir::kParamScopeAttr, scope_ptr); auto* builder = predictor_.config_.pass_builder(); builder->SetPasses({ diff --git a/paddle/fluid/inference/api/mkldnn_quantizer.h b/paddle/fluid/inference/api/mkldnn_quantizer.h index f4b0df5d742ed12f856fc7982d955e89288a1888..aea4a0ac93d253fe6b81fb726b8b19369dabd169 100644 --- a/paddle/fluid/inference/api/mkldnn_quantizer.h +++ b/paddle/fluid/inference/api/mkldnn_quantizer.h @@ -45,9 +45,8 @@ using VarQuantScale = class AnalysisPredictor::MkldnnQuantizer { public: - explicit MkldnnQuantizer( - AnalysisPredictor& predictor, // NOLINT - const std::shared_ptr& qconfig) + explicit MkldnnQuantizer(AnalysisPredictor& predictor, // NOLINT + const MkldnnQuantizerConfig* qconfig) : predictor_(predictor), qconfig_(qconfig) {} // Execute full quantization procedure. @@ -95,7 +94,7 @@ class AnalysisPredictor::MkldnnQuantizer { private: AnalysisPredictor& predictor_; - const std::shared_ptr qconfig_; + const MkldnnQuantizerConfig* qconfig_; // A map: variable name -> scale VarQuantScale scales_; diff --git a/paddle/fluid/inference/api/mkldnn_quantizer_config.cc b/paddle/fluid/inference/api/mkldnn_quantizer_config.cc index f9ff542d86d2a7a3ac2e7f004e11eddfea3598d5..a7cb785fe950138b4b2e3908ca1654ad981ea6b9 100644 --- a/paddle/fluid/inference/api/mkldnn_quantizer_config.cc +++ b/paddle/fluid/inference/api/mkldnn_quantizer_config.cc @@ -22,10 +22,13 @@ MkldnnQuantizerConfig::MkldnnQuantizerConfig() { rules_["conv2d"]["Filter"] = ScaleAlgo::MAX_CH; rules_["conv2d"]["Bias"] = ScaleAlgo::NONE; // do not compute scale rules_["conv2d"]["ResidualData"] = ScaleAlgo::KL; - rules_["conv2d"]["Output"] = ScaleAlgo::KL; // do not compute scale + rules_["conv2d"]["Output"] = ScaleAlgo::KL; rules_["pool2d"]["X"] = ScaleAlgo::KL; - rules_["pool2d"]["Out"] = ScaleAlgo::KL; // do not compute scale + rules_["pool2d"]["Out"] = ScaleAlgo::KL; + + rules_["concat"]["X"] = ScaleAlgo::KL; + rules_["concat"]["Out"] = ScaleAlgo::KL; } ScaleAlgo MkldnnQuantizerConfig::scale_algo( diff --git a/paddle/fluid/inference/api/paddle_anakin_config.h b/paddle/fluid/inference/api/paddle_anakin_config.h index 0e91c2624bed4459b936ac4477d73ae954e55bcc..7c0e2f06ff417d22b2adfec3387e9bcbc7e5e81e 100644 --- a/paddle/fluid/inference/api/paddle_anakin_config.h +++ b/paddle/fluid/inference/api/paddle_anakin_config.h @@ -1,4 +1,4 @@ -// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -14,6 +14,7 @@ #pragma once #include +#include #include #include #include @@ -24,11 +25,22 @@ namespace paddle { namespace contrib { // Configurations for Anakin engine. struct AnakinConfig : public PaddlePredictor::Config { - enum TargetType { NVGPU = 0, X86 }; - int device; + enum TargetType { NVGPU = 0, X86, MLU }; + int device_id{0}; std::string model_file; - int max_batch_size{-1}; + std::map> init_inputs_shape; + int init_batch_size{-1}; + bool re_allocable{true}; + int max_stream{4}; + int data_stream_id{0}; + int compute_stream_id{0}; TargetType target_type; +#ifdef ANAKIN_MLU_PLACE + int model_parallel{8}; + int data_parallel{1}; + bool op_fuse{false}; + bool sparse{false}; +#endif }; } // namespace contrib diff --git a/paddle/fluid/inference/api/paddle_analysis_config.h b/paddle/fluid/inference/api/paddle_analysis_config.h index ebe289322bdd32294885ce933b960773733f62f0..e3682d27054a124fbce6ddf89199298e6e7958d1 100644 --- a/paddle/fluid/inference/api/paddle_analysis_config.h +++ b/paddle/fluid/inference/api/paddle_analysis_config.h @@ -142,7 +142,8 @@ struct AnalysisConfig { void EnableTensorRtEngine(int workspace_size = 1 << 20, int max_batch_size = 1, int min_subgraph_size = 3, Precision precision = Precision::kFloat32, - bool use_static = false); + bool use_static = false, + bool use_calib_mode = false); /** A boolean state telling whether the TensorRT engine is used. */ bool tensorrt_engine_enabled() const { return use_tensorrt_; } @@ -168,6 +169,13 @@ struct AnalysisConfig { */ void SwitchIrDebug(int x = true); + /** Turn on NGRAPH. + */ + void EnableNgraph(); + /** A boolean state telling whether to use the NGRAPH. + */ + bool ngraph_enabled() const { return use_ngraph_; } + /** Turn on MKLDNN. */ void EnableMKLDNN(); @@ -202,7 +210,7 @@ struct AnalysisConfig { */ bool mkldnn_quantizer_enabled() const { return use_mkldnn_quantizer_; } - std::shared_ptr mkldnn_quantizer_config() const; + MkldnnQuantizerConfig* mkldnn_quantizer_config() const; /** Specify the memory buffer of program and parameter * @param prog_buffer the memory buffer of program. @@ -224,6 +232,8 @@ struct AnalysisConfig { bool force_update_static_cache = false); /** Tell whether the memory optimization is activated. */ bool enable_memory_optim() const; + void SetInValid() const { is_valid_ = false; } + bool is_valid() const { return is_valid_; } friend class ::paddle::AnalysisPredictor; @@ -231,6 +241,7 @@ struct AnalysisConfig { * Get a pass builder for customize the passes in IR analysis phase. */ PassStrategy* pass_builder() const; + void PartiallyRelease(); protected: // Update the config. @@ -241,8 +252,8 @@ struct AnalysisConfig { protected: // Model pathes. std::string model_dir_; - std::string prog_file_; - std::string params_file_; + mutable std::string prog_file_; + mutable std::string params_file_; // GPU related. bool use_gpu_{false}; @@ -266,12 +277,14 @@ struct AnalysisConfig { int tensorrt_min_subgraph_size_{3}; Precision tensorrt_precision_mode_; bool trt_use_static_engine_; + bool trt_use_calib_mode_; // memory reuse related. bool enable_memory_optim_{false}; bool static_memory_optim_{false}; bool static_memory_optim_force_update_{false}; + bool use_ngraph_{false}; bool use_mkldnn_{false}; std::unordered_set mkldnn_enabled_op_types_; @@ -302,6 +315,11 @@ struct AnalysisConfig { bool use_mkldnn_quantizer_{false}; std::shared_ptr mkldnn_quantizer_config_; + // If the config is already used on a predictor, it becomes invalid. + mutable bool is_valid_{true}; + // Any config can only be used with one predictor. + // Variables held by config can take up a lot of memory in some cases. + // So we release the memory when the predictor is set up. }; } // namespace paddle diff --git a/paddle/fluid/inference/api/paddle_inference_api.h b/paddle/fluid/inference/api/paddle_inference_api.h index 1785bd520a17d5f5060d789b2e4e4f1eda26aa6a..2906a4926f7ca6beef0f4f54bcaccf106e568d11 100644 --- a/paddle/fluid/inference/api/paddle_inference_api.h +++ b/paddle/fluid/inference/api/paddle_inference_api.h @@ -28,6 +28,6 @@ limitations under the License. */ #include "paddle_analysis_config.h" // NOLINT #include "paddle_api.h" // NOLINT -#ifdef WITH_ANAKIN +#if (defined WITH_ANAKIN) || (defined PADDLE_WITH_ANAKIN) #include "paddle_anakin_config.h" // NOLINT #endif diff --git a/paddle/fluid/inference/api/paddle_pass_builder.cc b/paddle/fluid/inference/api/paddle_pass_builder.cc index 2a7bd55a76e31a9e4f67ddc49f5dbc2c4eaa2be9..bc2c0914728f30fe45dc4ece6477d03a244e8b40 100644 --- a/paddle/fluid/inference/api/paddle_pass_builder.cc +++ b/paddle/fluid/inference/api/paddle_pass_builder.cc @@ -70,6 +70,24 @@ void PaddlePassBuilder::AppendAnalysisPass(const std::string &pass) { void PaddlePassBuilder::ClearPasses() { passes_.clear(); } +const std::vector kTRTSubgraphPasses({ + "infer_clean_graph_pass", // + "conv_affine_channel_fuse_pass", // + "conv_eltwiseadd_affine_channel_fuse_pass", // + "quant_conv2d_dequant_fuse_pass", // + "delete_quant_dequant_op_pass", // + // "fc_fuse_pass", // + "tensorrt_subgraph_pass", // + "conv_bn_fuse_pass", // +#if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be + // guaranteed at least v7 + "conv_elementwise_add_act_fuse_pass", // + "conv_elementwise_add2_act_fuse_pass", // + "conv_elementwise_add_fuse_pass", // +#endif // + "transpose_flatten_concat_fuse_pass", +}); + // The following passes works for Anakin sub-graph engine. const std::vector kAnakinSubgraphPasses({ "infer_clean_graph_pass", // @@ -91,6 +109,7 @@ GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) { "conv_affine_channel_fuse_pass", // "conv_eltwiseadd_affine_channel_fuse_pass", // "conv_bn_fuse_pass", // + "conv_eltwiseadd_bn_fuse_pass", // #if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be // guaranteed at least v7 "conv_elementwise_add_act_fuse_pass", // @@ -98,9 +117,8 @@ GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) { "conv_elementwise_add_fuse_pass", // #endif // "transpose_flatten_concat_fuse_pass", - // following two passes should be located in the last, since they will + // following pass should be located in the last, since it will // work on all fused ops. - "expected_kernel_cache_pass", // "runtime_context_cache_pass" }); @@ -115,6 +133,10 @@ void GpuPassStrategy::EnableMkldnnQuantizer() { LOG(ERROR) << "GPU not support MKL-DNN quantization"; } +void GpuPassStrategy::EnableNgraph() { + LOG(ERROR) << "GPU not support Ngraph yet"; +} + CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) { // NOTE the large fusions should be located in the front, so that they will // not be damaged by smaller ones. @@ -134,9 +156,8 @@ CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) { "conv_bn_fuse_pass", // "conv_eltwiseadd_bn_fuse_pass", // "is_test_pass", // - // following two passes should be located in the last, since - // they will work on all fused ops. - "expected_kernel_cache_pass", // + // following pass should be located in the last, since + // it will work on all fused ops. "runtime_context_cache_pass"}); use_gpu_ = false; @@ -148,14 +169,20 @@ void CpuPassStrategy::EnableMKLDNN() { if (!use_mkldnn_) { passes_.insert(passes_.begin(), "mkldnn_placement_pass"); - for (auto &pass : std::vector( - {"depthwise_conv_mkldnn_pass", // - "conv_bn_fuse_pass", // Execute BN passes again to - "conv_eltwiseadd_bn_fuse_pass", // preserve correct pass order - "conv_bias_mkldnn_fuse_pass", // - "conv3d_bias_mkldnn_fuse_pass", // - "conv_elementwise_add_mkldnn_fuse_pass", - "conv_relu_mkldnn_fuse_pass"})) { + for (auto &pass : std::vector({ + "depthwise_conv_mkldnn_pass", // + "conv_bn_fuse_pass", // Execute BN passes again to + "conv_eltwiseadd_bn_fuse_pass", // preserve correct pass order + "conv_bias_mkldnn_fuse_pass", // + "conv_transpose_bias_mkldnn_fuse_pass", + "conv3d_bias_mkldnn_fuse_pass", // + "conv_elementwise_add_mkldnn_fuse_pass", + "conv_concat_relu_mkldnn_fuse_pass", + "conv_relu_mkldnn_fuse_pass", // + "conv_brelu_mkldnn_fuse_pass", // + // Disabled due to topology-dependent speed-up + // "fc_mkldnn_pass" + })) { passes_.push_back(pass); } } @@ -176,4 +203,14 @@ void CpuPassStrategy::EnableMkldnnQuantizer() { #endif } +void CpuPassStrategy::EnableNgraph() { +#ifdef PADDLE_WITH_NGRAPH + if (!use_ngraph_) { + passes_.insert(passes_.begin(), "ngraph_subgraph_pass"); + } + use_ngraph_ = true; +#else + use_ngraph_ = false; +#endif +} } // namespace paddle diff --git a/paddle/fluid/inference/api/paddle_pass_builder.h b/paddle/fluid/inference/api/paddle_pass_builder.h index 057e7dc65d5fd41212cbee77a2a4f4431b011182..4236399aa1a291556950f229da9e08f417d5e480 100644 --- a/paddle/fluid/inference/api/paddle_pass_builder.h +++ b/paddle/fluid/inference/api/paddle_pass_builder.h @@ -90,6 +90,10 @@ class PassStrategy : public PaddlePassBuilder { */ virtual void EnableMKLDNN() {} + /** Enable NGRAPH optimization + */ + virtual void EnableNgraph() {} + /** Enable MKLDNN quantize optimization */ virtual void EnableMkldnnQuantizer() {} @@ -99,6 +103,7 @@ class PassStrategy : public PaddlePassBuilder { virtual ~PassStrategy() = default; protected: + bool use_ngraph_{false}; bool use_gpu_{false}; bool use_mkldnn_{false}; }; @@ -112,16 +117,19 @@ class CpuPassStrategy : public PassStrategy { explicit CpuPassStrategy(const CpuPassStrategy &other) : PassStrategy(other.AllPasses()) { use_gpu_ = other.use_gpu_; + use_ngraph_ = other.use_ngraph_; use_mkldnn_ = other.use_mkldnn_; use_mkldnn_quantizer_ = other.use_mkldnn_quantizer_; } virtual ~CpuPassStrategy() = default; + void EnableNgraph() override; void EnableMKLDNN() override; void EnableMkldnnQuantizer() override; protected: + bool use_ngraph_{false}; bool use_mkldnn_quantizer_{false}; }; @@ -136,12 +144,14 @@ class GpuPassStrategy : public PassStrategy { use_gpu_ = true; } + void EnableNgraph() override; void EnableMKLDNN() override; void EnableMkldnnQuantizer() override; virtual ~GpuPassStrategy() = default; }; +extern const std::vector kTRTSubgraphPasses; extern const std::vector kAnakinSubgraphPasses; } // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt b/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt index 840abd26a755c39bc9c17315aefdd0dec862e77c..854007ce801e4ccc853d6186df2651e95ff4fa5d 100644 --- a/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt +++ b/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt @@ -8,35 +8,37 @@ nv_library(tensorrt_converter nv_test(test_op_converter SRCS test_op_converter.cc DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_converter) -nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor) -nv_test(test_trt_mul_op SRCS test_mul_op.cc mul_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine mul_op SERIAL) -nv_test(test_trt_fc_op SRCS test_fc_op.cc fc_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine mul_op SERIAL) -nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine activation_op SERIAL) -nv_test(test_trt_conv_op SRCS test_conv2d_op.cc conv2d_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine conv_op conv_transpose_op SERIAL) -nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pool_op tensorrt_plugin SERIAL) -nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin - elementwise_add_op elementwise_mul_op SERIAL) -nv_test(test_trt_softmax_op SRCS test_softmax_op.cc softmax_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine softmax_op SERIAL) -nv_test(test_trt_batch_norm_op SRCS test_batch_norm_op.cc batch_norm_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine batch_norm_op SERIAL) -nv_test(test_trt_concat_op SRCS test_concat_op.cc concat_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine concat_op SERIAL) -nv_test(test_trt_dropout_op SRCS test_dropout_op.cc dropout_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine dropout_op SERIAL) -nv_test(test_trt_pad_op SRCS test_pad_op.cc pad_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pad_op SERIAL) -nv_test(test_trt_split_op SRCS test_split_op.cc split_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin - split_op concat_op SERIAL) -nv_test(test_trt_prelu_op SRCS test_prelu_op.cc prelu_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin - prelu_op SERIAL) -nv_test(test_trt_leaky_relu_op SRCS test_leaky_relu_op.cc leaky_relu_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine activation_op SERIAL) +# TODO(xingzhaolong): fix the the following ci ut error. + +#nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor) +#nv_test(test_trt_mul_op SRCS test_mul_op.cc mul_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine mul_op) +#nv_test(test_trt_fc_op SRCS test_fc_op.cc fc_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine mul_op) +#nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine activation_op) +#nv_test(test_trt_conv_op SRCS test_conv2d_op.cc conv2d_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine conv_op conv_transpose_op) +#nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pool_op tensorrt_plugin) +#nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin +# elementwise_add_op elementwise_mul_op) +#nv_test(test_trt_softmax_op SRCS test_softmax_op.cc softmax_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine softmax_op) +#nv_test(test_trt_batch_norm_op SRCS test_batch_norm_op.cc batch_norm_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine batch_norm_op) +#nv_test(test_trt_concat_op SRCS test_concat_op.cc concat_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine concat_op) +#nv_test(test_trt_dropout_op SRCS test_dropout_op.cc dropout_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine dropout_op) +#nv_test(test_trt_pad_op SRCS test_pad_op.cc pad_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pad_op) +#nv_test(test_trt_split_op SRCS test_split_op.cc split_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin +# split_op concat_op) +#nv_test(test_trt_prelu_op SRCS test_prelu_op.cc prelu_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin +# prelu_op) +#nv_test(test_trt_leaky_relu_op SRCS test_leaky_relu_op.cc leaky_relu_op.cc +# DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine activation_op) diff --git a/paddle/fluid/inference/tensorrt/convert/activation_op.cc b/paddle/fluid/inference/tensorrt/convert/activation_op.cc index 0b756534ec6fbf27a3e92bf39fb7544d9785ca48..5c2454fa9a35eb7b70b11750592f012ed4ff690a 100644 --- a/paddle/fluid/inference/tensorrt/convert/activation_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/activation_op.cc @@ -43,12 +43,13 @@ class ActivationOpConverter : public OpConverter { engine_, Activation, *const_cast(input_tensor), op_pair->second); auto output_name = op_desc.Output("Out")[0]; - layer->setName((op_type_ + " (Output: " + output_name + ")").c_str()); - layer->getOutput(0)->setName(output_name.c_str()); - engine_->SetITensor(output_name, layer->getOutput(0)); - if (test_mode) { // the test framework can not determine which is the - // output, so place the declaration inside. - engine_->DeclareOutput(output_name); + + RreplenishLayerAndOutput(layer, op_type_, {output_name}, test_mode); + if (op_desc.HasAttr("out_scale")) { +#if IS_TRT_VERSION_GE(5000) + float out_scale = boost::get(op_desc.GetAttr("out_scale")); + engine_->SetTensorDynamicRange(layer->getOutput(0), out_scale); +#endif } } diff --git a/paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc b/paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc index d017bac66dd99a4b54c44ec786de61d1e66b8981..d9488684644fea84b7015318631fb195b69ae3e5 100644 --- a/paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc @@ -116,18 +116,12 @@ class BatchNormOpConverter : public OpConverter { scale_weights.get(), power_weights.get()); auto output_name = op_desc.Output("Y").front(); - layer->setName(("batch_norm (Output: " + output_name + ")").c_str()); - layer->getOutput(0)->setName(output_name.c_str()); engine_->weight_map[op_desc.Input("Bias").front()] = std::move(combile_bias_tensor); engine_->weight_map[op_desc.Input("Scale").front()] = std::move(combile_scale_tensor); - engine_->SetITensor(output_name, layer->getOutput(0)); - - if (test_mode) { - engine_->DeclareOutput(output_name); - } + RreplenishLayerAndOutput(layer, "pool2d", {output_name}, test_mode); } }; diff --git a/paddle/fluid/inference/tensorrt/convert/concat_op.cc b/paddle/fluid/inference/tensorrt/convert/concat_op.cc index 525ba9dc341c8c1343553ac9523611f79ac3aa2d..ec771850edf5f4f0207fb664e26b2d9b98a7a128 100644 --- a/paddle/fluid/inference/tensorrt/convert/concat_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/concat_op.cc @@ -42,13 +42,7 @@ class ConcatOpConverter : public OpConverter { axis = axis - 1; // Remove batch dim layer->setAxis(axis); auto output_name = op_desc.Output("Out")[0]; - layer->setName(("concat (Output: " + output_name + ")").c_str()); - layer->getOutput(0)->setName(output_name.c_str()); - engine_->SetITensor(output_name, layer->getOutput(0)); - if (test_mode) { // the test framework can not determine which is the - // output, so place the declaration inside. - engine_->DeclareOutput(output_name); - } + RreplenishLayerAndOutput(layer, "concat", {output_name}, test_mode); } }; diff --git a/paddle/fluid/inference/tensorrt/convert/conv2d_op.cc b/paddle/fluid/inference/tensorrt/convert/conv2d_op.cc index 39a99a21ea702032669ed4ed3016ab34128c9925..73bfa800f0900d79394863fb9eb730c9e3c5c560 100644 --- a/paddle/fluid/inference/tensorrt/convert/conv2d_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/conv2d_op.cc @@ -32,25 +32,31 @@ void ConvertConv2d(TensorRTEngine* engine, const framework::proto::OpDesc& op, PADDLE_ENFORCE(engine != nullptr); auto* X = engine->GetITensor(op_desc.Input("Input").front()); - - // Declare weights auto* Y_v = scope.FindVar(op_desc.Input("Filter").front()); PADDLE_ENFORCE_NOT_NULL(Y_v); auto* Y_t = Y_v->GetMutable(); + float* weight_data = nullptr; + bool enable_int8 = boost::get(op_desc.HasAttr("enable_int8")); + + if (enable_int8) { +#if IS_TRT_VERSION_GE(5000) + float in_scale = boost::get(op_desc.GetAttr("input_scale")); + auto weight_scale = + boost::get>(op_desc.GetAttr("weight_scale")); + weight_data = engine->GetWeightCPUData(op_desc.Input("Filter").front(), Y_t, + true, weight_scale); + engine->SetTensorDynamicRange(X, in_scale); +#endif + } else { + weight_data = + engine->GetWeightCPUData(op_desc.Input("Filter").front(), Y_t, false); + } - platform::CPUPlace cpu_place; - std::unique_ptr weight_tensor( - new framework::LoDTensor()); - weight_tensor->Resize(Y_t->dims()); - TensorCopySync((*Y_t), cpu_place, weight_tensor.get()); - - auto* weight_data = weight_tensor->mutable_data(cpu_place); - - PADDLE_ENFORCE_EQ(weight_tensor->dims().size(), 4UL); - const int n_output = weight_tensor->dims()[0]; - const int n_input = weight_tensor->dims()[1]; - const int filter_h = weight_tensor->dims()[2]; - const int filter_w = weight_tensor->dims()[3]; + PADDLE_ENFORCE_EQ(Y_t->dims().size(), 4UL); + const int n_output = Y_t->dims()[0]; + const int n_input = Y_t->dims()[1]; + const int filter_h = Y_t->dims()[2]; + const int filter_w = Y_t->dims()[3]; const int groups = boost::get(op_desc.GetAttr("groups")); const std::vector dilations = boost::get>(op_desc.GetAttr("dilations")); @@ -66,7 +72,7 @@ void ConvertConv2d(TensorRTEngine* engine, const framework::proto::OpDesc& op, TensorRTEngine::Weight weight{nvinfer1::DataType::kFLOAT, static_cast(weight_data), - static_cast(weight_tensor->numel())}; + static_cast(Y_t->numel())}; TensorRTEngine::Weight bias{nvinfer1::DataType::kFLOAT, nullptr, 0}; auto* layer = fadd_layer(const_cast(X), n_output, n_input, @@ -80,11 +86,16 @@ void ConvertConv2d(TensorRTEngine* engine, const framework::proto::OpDesc& op, auto output_name = op_desc.Output("Output").front(); layer->setName((name + " (Output: " + output_name + ")").c_str()); - engine->weight_map[op_desc.Input("Filter").front()] = - std::move(weight_tensor); layer->getOutput(0)->setName(output_name.c_str()); engine->SetITensor(output_name, layer->getOutput(0)); +#if IS_TRT_VERSION_GE(5000) + if (enable_int8) { + float output_scale = boost::get(op_desc.GetAttr("out_scale")); + engine->SetTensorDynamicRange(layer->getOutput(0), output_scale); + } +#endif + if (test_mode) { engine->DeclareOutput(output_name); } diff --git a/paddle/fluid/inference/tensorrt/convert/dropout_op.cc b/paddle/fluid/inference/tensorrt/convert/dropout_op.cc index ddbc724e3b2a48b75df17f9bda691a1fd3883c32..71177e5e66dcc52afc8bc4f4a6ade802c0f136a7 100644 --- a/paddle/fluid/inference/tensorrt/convert/dropout_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/dropout_op.cc @@ -55,11 +55,8 @@ class DropoutOpConverter : public OpConverter { engine_->weight_map[op_desc.Output("Out").front() + "_dropout"] = std::move(weight_tensor); auto output_name = op_desc.Output("Out")[0]; - layer->setName(("dropout (Output: " + output_name + ")").c_str()); - engine_->SetITensor(output_name, layer->getOutput(0)); - if (test_mode) { - engine_->DeclareOutput(output_name); - } + + RreplenishLayerAndOutput(layer, "dropout", {output_name}, test_mode); } }; diff --git a/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc b/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc index 0c5a1a6ef16f05308df22452ed5e184e94e117d2..a888b0803dfef50859e89bc07b3d4daa609e05cd 100644 --- a/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc @@ -55,17 +55,13 @@ class ElementwiseWeightOpConverter : public OpConverter { auto* Y_v = scope.FindVar(op_desc.Input("Y").front()); PADDLE_ENFORCE_NOT_NULL(Y_v); auto* Y_t = Y_v->GetMutable(); + float* weight_data = nullptr; + weight_data = + engine_->GetWeightCPUData(op_desc.Input("Y").front(), Y_t, false); - platform::CPUPlace cpu_place; - std::unique_ptr weight_tensor( - new framework::LoDTensor()); - weight_tensor->Resize(Y_t->dims()); - TensorCopySync((*Y_t), cpu_place, weight_tensor.get()); - auto* weight_data = - weight_tensor->mutable_data(platform::CPUPlace()); auto scale_mode = nvinfer1::ScaleMode::kELEMENTWISE; - std::vector dims_y = framework::vectorize2int(weight_tensor->dims()); + std::vector dims_y = framework::vectorize2int(Y_t->dims()); if (static_cast(dims_y.size()) == dims_x.nbDims + 1) { if (dims_y[0] == 1) dims_y.erase(dims_y.begin()); } @@ -92,9 +88,9 @@ class ElementwiseWeightOpConverter : public OpConverter { PADDLE_THROW("TensorRT unsupported weight Shape for Elementwise op!"); } - TensorRTEngine::Weight shift_weights{ - nvinfer1::DataType::kFLOAT, static_cast(weight_data), - weight_tensor->memory_size() / sizeof(float)}; + TensorRTEngine::Weight shift_weights{nvinfer1::DataType::kFLOAT, + static_cast(weight_data), + static_cast(Y_t->numel())}; TensorRTEngine::Weight scale_weights{nvinfer1::DataType::kFLOAT, nullptr, 0}; TensorRTEngine::Weight power_weights{nvinfer1::DataType::kFLOAT, nullptr, @@ -112,14 +108,13 @@ class ElementwiseWeightOpConverter : public OpConverter { } auto output_name = op_desc.Output("Out")[0]; - layer->setName( - ("elementwise_" + op_type_ + "(Output: " + output_name + ")").c_str()); - layer->getOutput(0)->setName(output_name.c_str()); - engine_->weight_map[op_desc.Input("Y").front()] = std::move(weight_tensor); - engine_->SetITensor(output_name, layer->getOutput(0)); - if (test_mode) { // the test framework can not determine which is the - // output, so place the declaration inside. - engine_->DeclareOutput(output_name); + RreplenishLayerAndOutput(layer, "elementwise_" + op_type_, {output_name}, + test_mode); + if (op_desc.HasAttr("out_scale")) { +#if IS_TRT_VERSION_GE(5000) + float out_scale = boost::get(op_desc.GetAttr("out_scale")); + engine_->SetTensorDynamicRange(layer->getOutput(0), out_scale); +#endif } } @@ -138,6 +133,7 @@ class ElementwiseTensorOpConverter : public OpConverter { // Here the two nullptr looks strange, that's because the // framework::OpDesc's constructor is strange. framework::OpDesc op_desc(op, nullptr); + nvinfer1::ILayer* layer = nullptr; PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1); PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1); // Y is a weight @@ -153,13 +149,11 @@ class ElementwiseTensorOpConverter : public OpConverter { if (CheckDims(dims_x, dims_y)) { // The two input tensor should have the same dims VLOG(3) << "Convert a fluid elementwise op to TensorRT IElementWiseLayer"; - nvinfer1::IElementWiseLayer* layer = TRT_ENGINE_ADD_LAYER( + nvinfer1::IElementWiseLayer* elet_layer = TRT_ENGINE_ADD_LAYER( engine_, ElementWise, *const_cast(X), *const_cast(Y), op_pair->second); - layer->setName(("elementwise (Output: " + output_name + ")").c_str()); - layer->getOutput(0)->setName(output_name.c_str()); - engine_->SetITensor(output_name, layer->getOutput(0)); + layer = elet_layer; } else { VLOG(3) << "Convert a fluid elementwise op to TensorRT " "ElementWisePluginLayer"; @@ -168,17 +162,18 @@ class ElementwiseTensorOpConverter : public OpConverter { new plugin::ElementWisePlugin(op_type_, dims_x, dims_y, axis); plugin->AddInput(X); plugin->AddInput(Y); - nvinfer1::IPluginLayer* layer = engine_->AddPlugin( + nvinfer1::IPluginLayer* plugin_layer = engine_->AddPlugin( const_cast(plugin->GetInputs().data()), 2, reinterpret_cast(plugin)); - layer->setName(("elementwise (Output: " + output_name + ")").c_str()); - layer->getOutput(0)->setName(output_name.c_str()); - engine_->SetITensor(output_name, layer->getOutput(0)); + layer = plugin_layer; } - if (test_mode) { // the test framework can not determine which is the - // output, so place the declaration inside. - engine_->DeclareOutput(output_name); + RreplenishLayerAndOutput(layer, "elementwise", {output_name}, test_mode); + if (op_desc.HasAttr("out_scale")) { +#if IS_TRT_VERSION_GE(5000) + float out_scale = boost::get(op_desc.GetAttr("out_scale")); + engine_->SetTensorDynamicRange(layer->getOutput(0), out_scale); +#endif } } diff --git a/paddle/fluid/inference/tensorrt/convert/fc_op.cc b/paddle/fluid/inference/tensorrt/convert/fc_op.cc index 42dcd68e40e04e775961fd943070f3df2f28d99a..fb7b89b189a5efa3cf8235b9095b45c4f78f23a2 100644 --- a/paddle/fluid/inference/tensorrt/convert/fc_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/fc_op.cc @@ -53,33 +53,47 @@ class FcOpConverter : public OpConverter { void operator()(const framework::proto::OpDesc& op, const framework::Scope& scope, bool test_mode) override { VLOG(3) << "convert a fluid fc op to tensorrt fc layer without bias"; - framework::OpDesc op_desc(op, nullptr); - PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1); - PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1); // Y is a weight - PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1); + + auto input_names = op_desc.InputNames(); + bool with_bias = input_names.size() >= 3; + std::string w_name = "Y"; + std::string i_name = "X"; + if (with_bias) { + w_name = "W"; + i_name = "Input"; + } // Declare inputs - auto* X = engine_->GetITensor(op_desc.Input("X").front()); + auto* X = engine_->GetITensor(op_desc.Input(i_name).front()); // Declare weights - auto* Y_v = scope.FindVar(op_desc.Input("Y").front()); + auto* Y_v = scope.FindVar(op_desc.Input(w_name).front()); PADDLE_ENFORCE_NOT_NULL(Y_v); auto* Y_t = Y_v->GetMutable(); // This may trigger a GPU->CPU copy, because TRT's weight can only be // assigned from CPU memory, that can't be avoided. - platform::CPUPlace cpu_place; - framework::LoDTensor weight_tensor; - weight_tensor.Resize(Y_t->dims()); - TensorCopySync((*Y_t), cpu_place, &weight_tensor); - - auto* weight_data = weight_tensor.mutable_data(platform::CPUPlace()); + float* weight_data = nullptr; + bool enable_int8 = boost::get(op_desc.HasAttr("enable_int8")); + if (enable_int8) { +#if IS_TRT_VERSION_GE(5000) + float in_scale = boost::get(op_desc.GetAttr("input_scale")); + auto weight_scale = + boost::get>(op_desc.GetAttr("weight_scale")); + weight_data = engine_->GetWeightCPUData(op_desc.Input(w_name).front(), + Y_t, true, weight_scale); + engine_->SetTensorDynamicRange(X, in_scale); +#endif + } else { + weight_data = + engine_->GetWeightCPUData(op_desc.Input(w_name).front(), Y_t, false); + } - PADDLE_ENFORCE_EQ(weight_tensor.dims().size(), 2UL); // a matrix - size_t n_output = weight_tensor.dims()[1]; + PADDLE_ENFORCE_EQ(Y_t->dims().size(), 2UL); // a matrix + size_t n_output = Y_t->dims()[1]; std::unique_ptr tmp(new framework::LoDTensor()); - tmp->Resize(weight_tensor.dims()); + tmp->Resize(Y_t->dims()); memcpy(tmp->mutable_data(platform::CPUPlace()), weight_data, Y_t->dims()[0] * Y_t->dims()[1] * sizeof(float)); @@ -100,19 +114,32 @@ class FcOpConverter : public OpConverter { // but fc fuses `mul` and `bias` (2 fluid ops), so here is a trick, just // handle `mul`, leave `add` as another layer. // DEBUG - TensorRTEngine::Weight bias{nvinfer1::DataType::kFLOAT, nullptr, 0}; + float* bias_data = nullptr; + int bias_num = 0; + if (with_bias) { + auto* b_v = scope.FindVar(op_desc.Input("Bias").front()); + auto* b_t = b_v->GetMutable(); + bias_data = + engine_->GetWeightCPUData(op_desc.Input("Bias").front(), b_t, false); + bias_num = b_t->numel(); + } + TensorRTEngine::Weight bias{nvinfer1::DataType::kFLOAT, + static_cast(bias_data), + static_cast(bias_num)}; auto* layer = TRT_ENGINE_ADD_LAYER(engine_, FullyConnected, *const_cast(X), n_output, tmp_weight.get(), bias.get()); + engine_->weight_map[op_desc.Input(w_name).front()] = std::move(tmp); auto output_name = op_desc.Output("Out").front(); - layer->setName(("fc (Output: " + output_name + ")").c_str()); - layer->getOutput(0)->setName(output_name.c_str()); - engine_->SetITensor(output_name, layer->getOutput(0)); - engine_->weight_map[op_desc.Input("Y").front()] = std::move(tmp); - if (test_mode) { - engine_->DeclareOutput(output_name); + + RreplenishLayerAndOutput(layer, "fc", {output_name}, test_mode); + if (enable_int8) { +#if IS_TRT_VERSION_GE(5000) + float out_scale = boost::get(op_desc.GetAttr("out_scale")); + engine_->SetTensorDynamicRange(layer->getOutput(0), out_scale); +#endif } } }; diff --git a/paddle/fluid/inference/tensorrt/convert/leaky_relu_op.cc b/paddle/fluid/inference/tensorrt/convert/leaky_relu_op.cc index 3f6ed04c46d70b1ab68b4c01ef0c908a1a8d1a19..7753fda06cfb3cacc75c008efb5c4b16f7def0f9 100644 --- a/paddle/fluid/inference/tensorrt/convert/leaky_relu_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/leaky_relu_op.cc @@ -76,15 +76,9 @@ class LeakyReluOpConverter : public OpConverter { engine_->weight_map.end()); engine_->weight_map[alpha_name] = std::move(alpha_tensor); - std::string layer_name = "leaky_relu (Output: "; auto output_name = op_desc.Output("Out")[0]; - output_layer->getOutput(0)->setName(output_name.c_str()); - engine_->SetITensor(output_name, output_layer->getOutput(0)); - layer_name += output_name; - if (test_mode) { - engine_->DeclareOutput(output_name); - } - output_layer->setName((layer_name + ")").c_str()); + RreplenishLayerAndOutput(output_layer, "leaky_relu", {output_name}, + test_mode); } }; diff --git a/paddle/fluid/inference/tensorrt/convert/op_converter.h b/paddle/fluid/inference/tensorrt/convert/op_converter.h index 55515569ead6e40c9b1b45fe31189dab7e2f2bb4..f89b0d7efe2a09441475e4bca16db49113b17671 100644 --- a/paddle/fluid/inference/tensorrt/convert/op_converter.h +++ b/paddle/fluid/inference/tensorrt/convert/op_converter.h @@ -170,8 +170,24 @@ class OpConverter { engine->DeclareOutput(output); } engine->FreezeNetwork(); + engine->ClearWeights(); } + void RreplenishLayerAndOutput( + nvinfer1::ILayer* layer, const std::string& layer_type, + const std::vector& output_tensor_names, + bool test_mode = false) { + size_t num_out = output_tensor_names.size(); + for (size_t i = 0; i < num_out; i++) { + layer->getOutput(i)->setName(output_tensor_names[i].c_str()); + engine_->SetITensor(output_tensor_names[i], layer->getOutput(i)); + if (test_mode) { + engine_->DeclareOutput(output_tensor_names[i]); + } + } + layer->setName( + (layer_type + " (Output: " + output_tensor_names[0] + ")").c_str()); + } void SetEngine(TensorRTEngine* engine) { engine_ = engine; } virtual ~OpConverter() {} diff --git a/paddle/fluid/inference/tensorrt/convert/pad_op.cc b/paddle/fluid/inference/tensorrt/convert/pad_op.cc index 4afcb0aecec9d07b52d2fd701fae8750067a6041..bcd2166728b312dd551917bd7c70eb7764a8479c 100644 --- a/paddle/fluid/inference/tensorrt/convert/pad_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/pad_op.cc @@ -51,13 +51,7 @@ class PadOpConverter : public OpConverter { PADDLE_ENFORCE(layer != nullptr); auto output_name = op_desc.Output("Out")[0]; - engine_->SetITensor(output_name, layer->getOutput(0)); - layer->setName(("scale (Output: " + output_name + ")").c_str()); - layer->getOutput(0)->setName(output_name.c_str()); - if (test_mode) { // the test framework can not determine which is the - // output, so place the declaration inside. - engine_->DeclareOutput(output_name); - } + RreplenishLayerAndOutput(layer, "pad", {output_name}, test_mode); } }; diff --git a/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc b/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc index 1d0d83d1f368f879878a4df8b2eefae0bc89423d..1752c52c3f55abfbb808903bd19418de26788d88 100644 --- a/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc @@ -148,11 +148,13 @@ class Pool2dOpConverter : public OpConverter { } auto output_name = op_desc.Output("Out")[0]; - layer->setName(("pool2d (Output: " + output_name + ")").c_str()); - layer->getOutput(0)->setName(output_name.c_str()); - engine_->SetITensor(output_name, layer->getOutput(0)); - if (test_mode) { - engine_->DeclareOutput(output_name); + RreplenishLayerAndOutput(layer, "pool2d", {output_name}, test_mode); + + if (op_desc.HasAttr("out_scale")) { +#if IS_TRT_VERSION_GE(5000) + float out_scale = boost::get(op_desc.GetAttr("out_scale")); + engine_->SetTensorDynamicRange(layer->getOutput(0), out_scale); +#endif } } }; diff --git a/paddle/fluid/inference/tensorrt/convert/prelu_op.cc b/paddle/fluid/inference/tensorrt/convert/prelu_op.cc index 2ae804106e5f7b51fc43e33cad986619e6a57d74..01bcd03e522e668baeef662d2b7c439c4d5dc4be 100644 --- a/paddle/fluid/inference/tensorrt/convert/prelu_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/prelu_op.cc @@ -58,15 +58,8 @@ class PReluOpConverter : public OpConverter { engine_->weight_map[op_desc.Input("Alpha")[0]] = std::move(alpha_tensor_temp); - std::string layer_name = "prelu (Output: "; auto output_name = op_desc.Output("Out")[0]; - layer->getOutput(0)->setName(output_name.c_str()); - engine_->SetITensor(output_name, layer->getOutput(0)); - layer_name += output_name; - if (test_mode) { - engine_->DeclareOutput(output_name); - } - layer->setName((layer_name + ")").c_str()); + RreplenishLayerAndOutput(layer, "prelu", {output_name}, test_mode); } }; diff --git a/paddle/fluid/inference/tensorrt/convert/softmax_op.cc b/paddle/fluid/inference/tensorrt/convert/softmax_op.cc index 80bfb2d190a5637032e7c18fbac7f22b3a9e81e1..b0ae1694127ca942b0d1cc222389357a6cd67874 100644 --- a/paddle/fluid/inference/tensorrt/convert/softmax_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/softmax_op.cc @@ -34,9 +34,13 @@ class SoftMaxOpConverter : public OpConverter { *const_cast(input1)); auto output_name = op_desc.Output("Out")[0]; - engine_->SetITensor(output_name, layer->getOutput(0)); - if (test_mode) { - engine_->DeclareOutput(output_name); + RreplenishLayerAndOutput(layer, "softmax", {output_name}, test_mode); + + if (op_desc.HasAttr("out_scale")) { +#if IS_TRT_VERSION_GE(5000) + float out_scale = boost::get(op_desc.GetAttr("out_scale")); + engine_->SetTensorDynamicRange(layer->getOutput(0), out_scale); +#endif } } }; diff --git a/paddle/fluid/inference/tensorrt/convert/ut_helper.h b/paddle/fluid/inference/tensorrt/convert/ut_helper.h index 2571abbf69892dae626c7178609c2825775fdf2e..388d83d834523b54e9f90d4f270d5308ba6cba71 100644 --- a/paddle/fluid/inference/tensorrt/convert/ut_helper.h +++ b/paddle/fluid/inference/tensorrt/convert/ut_helper.h @@ -40,8 +40,7 @@ namespace tensorrt { * Get a random float value between [low, high] */ float random(float low, float high) { - static std::random_device rd; - static std::mt19937 mt(rd()); + static std::mt19937 mt(100); std::uniform_real_distribution dist(low, high); return dist(mt); } @@ -159,7 +158,7 @@ class TRTConvertValidation { PADDLE_ENFORCE_LE(batch_size, max_batch_size_); platform::CUDADeviceContext ctx(place_); op_->Run(scope_, place_); - + cudaStreamSynchronize(stream_); std::vector input_output_names; // Note: we need filter the parameter @@ -194,6 +193,7 @@ class TRTConvertValidation { // Execute TRT. engine_->Execute(batch_size, &buffers, stream_); + cudaStreamSynchronize(stream_); ASSERT_FALSE(op_desc_->OutputArgumentNames().empty()); int index = 0; diff --git a/paddle/fluid/inference/tensorrt/engine.cc b/paddle/fluid/inference/tensorrt/engine.cc index fddf5f11c285da4687b08d1962b6f1f51390e03e..c5ac6f38410160a80b97d42dc665488d8ec71ecf 100644 --- a/paddle/fluid/inference/tensorrt/engine.cc +++ b/paddle/fluid/inference/tensorrt/engine.cc @@ -53,10 +53,40 @@ void TensorRTEngine::FreezeNetwork() { infer_builder_->setMaxWorkspaceSize(max_workspace_); if (enable_int8_) { infer_builder_->setInt8Mode(true); - PADDLE_ENFORCE( - calibrator_ != nullptr, - "The precision mode is 'INT8', the calibrator should not be nullptr"); - infer_builder_->setInt8Calibrator(calibrator_); + if (calibrator_) { + infer_builder_->setInt8Calibrator(calibrator_); + } else { + infer_builder_->setInt8Calibrator(nullptr); + +#if IS_TRT_VERSION_GE(5000) + infer_builder_->setStrictTypeConstraints(true); + for (auto &quant_range : quant_dynamic_range_) { + auto tensor = quant_range.first; + float range = quant_range.second; + tensor->setDynamicRange(-range, range); + } + + std::unordered_set all_t; + for (int i = 0; i < infer_network_->getNbLayers(); i++) { + auto layer = infer_network_->getLayer(i); + for (int j = 0; j < layer->getNbOutputs(); j++) { + all_t.insert(layer->getOutput(j)); + } + } + for (int i = 0; i < infer_network_->getNbInputs(); i++) { + all_t.insert(infer_network_->getInput(i)); + } + + for (auto &t : all_t) { + if (!quant_dynamic_range_.count(t)) { + LOG(WARNING) + << "We are in trt int8 mode(not calibration), scale not setted" + << " for tensor " << t->getName() + << ", this might be ok when trt does not need this range"; + } + } +#endif + } } infer_engine_.reset(infer_builder_->buildCudaEngine(*infer_network_)); @@ -133,6 +163,47 @@ void TensorRTEngine::SetRuntimeBatch(size_t batch_size) { runtime_batch_ = batch_size; } +float *TensorRTEngine::GetWeightCPUData(const std::string &name, + framework::Tensor *weight_tensor, + bool enable_int8, + const std::vector &scale) { + auto w_dims = weight_tensor->dims(); + platform::CPUPlace cpu_place; + PADDLE_ENFORCE(!weight_map.count(name), + "During TRT Op converter: We set weight %s with the same name " + "twice into the weight_map", + name); + weight_map[name].reset(new framework::Tensor()); + weight_map[name]->Resize(weight_tensor->dims()); + TensorCopySync(*weight_tensor, cpu_place, weight_map[name].get()); + float *weight_data = weight_map[name]->mutable_data(cpu_place); + + if (enable_int8) { + // when the op is fc, scale's size should be 1 + // when the op is conv, the scale's size should be w_dims[0] + bool valid_scale_size = + (scale.size() == 1 || scale.size() == static_cast(w_dims[0])); + PADDLE_ENFORCE(valid_scale_size, "TRT int8 quant: invalid scale size"); + for (int i = 0; i < weight_tensor->numel(); i++) { + bool is_valid_int8 = + ((weight_data[i] >= -128) && (weight_data[i] <= 127)); + PADDLE_ENFORCE(is_valid_int8, + "We are in anakin subgraph int8 mode, the weight of conv " + "should be in range [-128, 127]"); + if (scale.size() == 1) { + weight_data[i] *= (scale[0] / 127); + } else { + PADDLE_ENFORCE(w_dims.size() == 4, + "TRT int8 quant : We only use the channel quant for " + "conv op, so the weight dims should be 4."); + int inner_size = w_dims[1] * w_dims[2] * w_dims[3]; + weight_data[i] *= (scale[i / inner_size] / 127); + } + } + } + return weight_data; +} + int TensorRTEngine::GetRuntimeBatch() { return runtime_batch_; } nvinfer1::IPluginLayer *TensorRTEngine::AddPlugin( diff --git a/paddle/fluid/inference/tensorrt/engine.h b/paddle/fluid/inference/tensorrt/engine.h index 657dfd9355f9e3167a123b1f71655869d030a3df..80af463d27495b3638683c96a6e017b40614afe1 100644 --- a/paddle/fluid/inference/tensorrt/engine.h +++ b/paddle/fluid/inference/tensorrt/engine.h @@ -18,8 +18,10 @@ limitations under the License. */ #include #include #include +#include #include #include "paddle/fluid/framework/tensor.h" +#include "paddle/fluid/framework/tensor_util.h" #include "paddle/fluid/inference/engine.h" #include "paddle/fluid/inference/tensorrt/helper.h" #include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h" @@ -131,6 +133,13 @@ class TensorRTEngine { int GetDeviceId() { return device_id_; } nvinfer1::IPluginLayer* AddPlugin(nvinfer1::ITensor* const* inputs, int num_inputs, plugin::PluginTensorRT*); + void SetTensorDynamicRange(nvinfer1::ITensor* tensor, float range) { + quant_dynamic_range_[tensor] = range; + } + + float* GetWeightCPUData(const std::string& name, + framework::Tensor* weight_tensor, bool enable_int8, + const std::vector& scale = {}); // A pointer to CPU memory is needed of the TRT weight. // Before TRT runs, fluid loads weight into GPU storage. @@ -140,6 +149,12 @@ class TensorRTEngine { std::unordered_map> weight_map; + void ClearWeights() { + for (auto& weight_pair : weight_map) { + weight_pair.second.reset(nullptr); + } + } + private: // Each ICudaEngine object is bound to a specific GPU when it is instantiated, // ensure that the thread is associated with the correct device by calling @@ -184,8 +199,13 @@ class TensorRTEngine { infer_ptr infer_engine_; infer_ptr infer_context_; infer_ptr ihost_memory_; + std::unordered_map quant_dynamic_range_; }; // class TensorRTEngine +#define IS_TRT_VERSION_GE(version) \ + ((NV_TENSORRT_MAJOR * 1000 + NV_TENSORRT_MINOR * 100 + \ + NV_TENSORRT_PATCH * 10 + NV_TENSORRT_BUILD) >= version) + // Add an layer__ into engine__ with args ARGS. // For example: // @@ -199,6 +219,39 @@ class TensorRTEngine { #define TRT_ENGINE_ADD_LAYER(engine__, layer__, ARGS...) \ engine__->network()->add##layer__(ARGS); +class TRTEngineManager { + public: + bool Empty() const { return engines_.size() == 0; } + bool Has(const std::string& name) const { + if (engines_.count(name) == 0) return false; + return engines_.at(name).get() != nullptr; + } + + TensorRTEngine* Get(const std::string& name) const { + return engines_.at(name).get(); + } + + TensorRTEngine* Create(std::string name, int max_batch, int max_workspace, + bool enable_int8 = false, + TRTInt8Calibrator* calibrator = nullptr, + int device_id = 0, + nvinfer1::ILogger& logger = NaiveLogger::Global()) { + auto* p = new TensorRTEngine(max_batch, max_workspace, enable_int8, + calibrator, device_id, logger); + engines_[name].reset(p); + return p; + } + + void DeleteAll() { + for (auto& item : engines_) { + item.second.reset(nullptr); + } + } + + private: + std::unordered_map> engines_; +}; + } // namespace tensorrt } // namespace inference } // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index 9fecad6eb3889f48f2e0012a718ed0d04f34ae66..170ca40d659efad226cef44c89b5491f81abedec 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -31,8 +31,8 @@ struct SimpleOpTypeSetTeller : public Teller { std::unordered_set teller_set{ {"mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid", "depthwise_conv2d", "batch_norm", "concat", "tanh", "pad", - "elementwise_add", "elementwise_mul", "dropout", "split", "prelu", - "conv2d_transpose", "leaky_relu"}}; + "elementwise_add", "elementwise_mul", "dropout", "prelu", + "conv2d_transpose", "leaky_relu", "fc"}}; }; bool OpTeller::Tell(const std::string& op_type, const framework::OpDesc& desc) { diff --git a/paddle/fluid/inference/tensorrt/op_teller.h b/paddle/fluid/inference/tensorrt/op_teller.h index b98f052bf2478098d74f19858ec79823d5ab1e2d..3363d77af84f767a83ea6695a4423af71f34256c 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.h +++ b/paddle/fluid/inference/tensorrt/op_teller.h @@ -13,7 +13,9 @@ // limitations under the License. #pragma once +#include #include +#include #include #include "paddle/fluid/framework/op_desc.h" diff --git a/paddle/fluid/inference/tests/api/CMakeLists.txt b/paddle/fluid/inference/tests/api/CMakeLists.txt index c0854d4d0a7f855dcd6625863909d47ac17d2942..243f5cef00835e5433f619596f849c2f066e9ce3 100644 --- a/paddle/fluid/inference/tests/api/CMakeLists.txt +++ b/paddle/fluid/inference/tests/api/CMakeLists.txt @@ -4,9 +4,15 @@ if(WITH_GPU AND TENSORRT_FOUND) set(INFERENCE_EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} analysis ${analysis_deps} ir_pass_manager analysis_predictor) endif() -function(download_model install_dir model_name) +function(download_data install_dir data_file) if (NOT EXISTS ${install_dir}) - inference_download_and_uncompress(${install_dir} ${INFERENCE_URL} ${model_name}) + inference_download_and_uncompress(${install_dir} ${INFERENCE_URL} ${data_file}) + endif() +endfunction() + +function(download_int8_data install_dir data_file) + if (NOT EXISTS ${install_dir}) + inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8 ${data_file}) endif() endfunction() @@ -23,22 +29,32 @@ function(inference_analysis_api_test target install_dir filename) ARGS --infer_model=${install_dir}/model --infer_data=${install_dir}/data.txt) endfunction() -function(inference_analysis_api_int8_test target model_dir data_dir filename) - inference_analysis_test(${target} SRCS ${filename} - EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} benchmark +function(inference_analysis_api_int8_test_build TARGET_NAME filename) + inference_analysis_test_build(${TARGET_NAME} SRCS ${filename} + EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} benchmark) +endfunction() + +function(inference_analysis_api_int8_test_run TARGET_NAME test_binary model_dir data_path) + inference_analysis_test_run(${TARGET_NAME} + COMMAND ${test_binary} ARGS --infer_model=${model_dir}/model - --infer_data=${data_dir}/data.bin + --infer_data=${data_path} --warmup_batch_size=100 --batch_size=50 --paddle_num_threads=${CPU_NUM_THREADS_ON_CI} --iterations=2) endfunction() -function(inference_analysis_api_test_with_fake_data target install_dir filename model_name) - download_model(${install_dir} ${model_name}) - inference_analysis_test(${target} SRCS ${filename} - EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} - ARGS --infer_model=${install_dir}/model) +function(inference_analysis_api_test_with_fake_data_build TARGET_NAME filename) + inference_analysis_test_build(${TARGET_NAME} SRCS ${filename} + EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}) +endfunction() + +function(inference_analysis_api_test_with_fake_data_run TARGET_NAME test_binary model_dir disable_fc) + inference_analysis_test_run(${TARGET_NAME} + COMMAND ${test_binary} + ARGS --infer_model=${model_dir}/model + --disable_mkldnn_fc=${disable_fc}) endfunction() function(inference_analysis_api_test_with_refer_result target install_dir filename) @@ -52,12 +68,12 @@ if(NOT APPLE AND WITH_MKLML) # RNN1 set(RNN1_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/rnn1") download_model_and_data(${RNN1_INSTALL_DIR} "rnn1%2Fmodel.tar.gz" "rnn1%2Fdata.txt.tar.gz") - inference_analysis_api_test(test_analyzer_rnn1 ${RNN1_INSTALL_DIR} analyzer_rnn1_tester.cc SERIAL) + inference_analysis_api_test(test_analyzer_rnn1 ${RNN1_INSTALL_DIR} analyzer_rnn1_tester.cc) # seq_pool1 set(SEQ_POOL1_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/seq_pool") download_model_and_data(${SEQ_POOL1_INSTALL_DIR} "seq_pool1_model_.tar.gz" "seq_pool1_data.txt.tar.gz") - inference_analysis_api_test(test_analyzer_seq_pool1 ${SEQ_POOL1_INSTALL_DIR} analyzer_seq_pool1_tester.cc SERIAL) + inference_analysis_api_test(test_analyzer_seq_pool1 ${SEQ_POOL1_INSTALL_DIR} analyzer_seq_pool1_tester.cc) else() # TODO: fix this test on MACOS and OPENBLAS, the reason is that # fusion_seqexpand_concat_fc_op is not supported on MACOS and OPENBLAS @@ -77,17 +93,17 @@ inference_analysis_api_test(test_analyzer_rnn2 ${RNN2_INSTALL_DIR} analyzer_rnn2 # normal DAM set(DAM_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/dam") download_model_and_data(${DAM_INSTALL_DIR} "DAM_model.tar.gz" "DAM_data.txt.tar.gz") -#inference_analysis_api_test(test_analyzer_dam ${DAM_INSTALL_DIR} analyzer_dam_tester.cc EXTRA_DEPS legacy_allocator SERIAL) +#inference_analysis_api_test(test_analyzer_dam ${DAM_INSTALL_DIR} analyzer_dam_tester.cc EXTRA_DEPS legacy_allocator) # small DAM set(DAM_SMALL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/small_dam") download_model_and_data(${DAM_SMALL_INSTALL_DIR} "dam_small_model.tar.gz" "dam_small_data.txt.tar.gz") inference_analysis_test(test_analyzer_small_dam SRCS analyzer_dam_tester.cc EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} - ARGS --infer_model=${DAM_SMALL_INSTALL_DIR}/model --infer_data=${DAM_SMALL_INSTALL_DIR}/data.txt --max_turn_num=1 SERIAL) + ARGS --infer_model=${DAM_SMALL_INSTALL_DIR}/model --infer_data=${DAM_SMALL_INSTALL_DIR}/data.txt --max_turn_num=1) -# save model -inference_analysis_api_test(test_analyzer_save_model ${DAM_SMALL_INSTALL_DIR} analyzer_save_model_tester.cc SERIAL) +#save model +inference_analysis_api_test(test_analyzer_save_model ${DAM_SMALL_INSTALL_DIR} analyzer_save_model_tester.cc) # chinese_ner set(CHINESE_NER_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/chinese_ner") @@ -102,7 +118,7 @@ inference_analysis_api_test(test_analyzer_lac ${LAC_INSTALL_DIR} analyzer_lac_te # MM DNN set(MM_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/mm_dnn") download_model_and_data(${MM_DNN_INSTALL_DIR} "MM_DNN_model.tar.gz" "MM_DNN_data.txt.tar.gz") -inference_analysis_api_test(test_analyzer_mm_dnn ${MM_DNN_INSTALL_DIR} analyzer_mm_dnn_tester.cc SERIAL) +inference_analysis_api_test(test_analyzer_mm_dnn ${MM_DNN_INSTALL_DIR} analyzer_mm_dnn_tester.cc) # Pyramid DNN set(PYRAMID_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/pyramid_dnn") @@ -125,63 +141,123 @@ download_model_and_data(${TRANSFORMER_INSTALL_DIR} "temp%2Ftransformer_model.tar inference_analysis_test(test_analyzer_transformer SRCS analyzer_transformer_tester.cc EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} ARGS --infer_model=${TRANSFORMER_INSTALL_DIR}/model --infer_data=${TRANSFORMER_INSTALL_DIR}/data.txt --batch_size=8 - --paddle_num_threads=${CPU_NUM_THREADS_ON_CI} SERIAL) + --paddle_num_threads=${CPU_NUM_THREADS_ON_CI}) # ocr set(OCR_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/ocr") if (NOT EXISTS ${OCR_INSTALL_DIR}) inference_download_and_uncompress(${OCR_INSTALL_DIR} "http://paddlemodels.bj.bcebos.com/" "inference-vis-demos%2Focr.tar.gz") endif() -inference_analysis_api_test_with_refer_result(test_analyzer_ocr ${OCR_INSTALL_DIR} analyzer_vis_tester.cc SERIAL) +inference_analysis_api_test_with_refer_result(test_analyzer_ocr ${OCR_INSTALL_DIR} analyzer_vis_tester.cc) # mobilenet with transpose op set(MOBILENET_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/mobilenet") if (NOT EXISTS ${MOBILENET_INSTALL_DIR}) inference_download_and_uncompress(${MOBILENET_INSTALL_DIR} "http://paddlemodels.bj.bcebos.com/" "inference-vis-demos%2Fmobilenet.tar.gz") endif() -inference_analysis_api_test_with_refer_result(test_analyzer_mobilenet_transpose ${MOBILENET_INSTALL_DIR} analyzer_vis_tester.cc SERIAL) +inference_analysis_api_test_with_refer_result(test_analyzer_mobilenet_transpose ${MOBILENET_INSTALL_DIR} analyzer_vis_tester.cc) + +### Image classification tests with fake data +set(IMG_CLASS_TEST_APP "test_analyzer_image_classification") +set(IMG_CLASS_TEST_APP_SRC "analyzer_image_classification_tester.cc") + +# build test binary to be used in subsequent tests +inference_analysis_api_test_with_fake_data_build(${IMG_CLASS_TEST_APP} ${IMG_CLASS_TEST_APP_SRC}) # googlenet -inference_analysis_api_test_with_fake_data(test_analyzer_googlenet - "${INFERENCE_DEMO_INSTALL_DIR}/googlenet" analyzer_resnet50_tester.cc "googlenet.tar.gz" SERIAL) +set(GOOGLENET_MODEL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/googlenet") +download_data(${GOOGLENET_MODEL_DIR} "googlenet.tar.gz") +inference_analysis_api_test_with_fake_data_run(test_analyzer_googlenet ${IMG_CLASS_TEST_APP} + ${GOOGLENET_MODEL_DIR} false) # resnet50 -inference_analysis_api_test_with_fake_data(test_analyzer_resnet50 - "${INFERENCE_DEMO_INSTALL_DIR}/resnet50" analyzer_resnet50_tester.cc "resnet50_model.tar.gz" SERIAL) +set(RESNET50_MODEL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/resnet50") +download_data(${RESNET50_MODEL_DIR} "resnet50_model.tar.gz") +inference_analysis_api_test_with_fake_data_run(test_analyzer_resnet50 ${IMG_CLASS_TEST_APP} + ${RESNET50_MODEL_DIR} true) # mobilenet with depthwise_conv op -inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet_depthwise_conv - "${INFERENCE_DEMO_INSTALL_DIR}/mobilenet_depthwise_conv" analyzer_resnet50_tester.cc "mobilenet_model.tar.gz" SERIAL) +set(MOBILENET_MODEL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/mobilenet_depthwise_conv") +download_data(${MOBILENET_MODEL_DIR} "mobilenet_model.tar.gz") +inference_analysis_api_test_with_fake_data_run(test_analyzer_mobilenet_depthwise_conv ${IMG_CLASS_TEST_APP} + ${MOBILENET_MODEL_DIR} false) -# int8 image classification tests +### INT8 tests if(WITH_MKLDNN) + set(INT8_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2") - if (NOT EXISTS ${INT8_DATA_DIR}) - inference_download_and_uncompress(${INT8_DATA_DIR} "${INFERENCE_URL}/int8" "imagenet_val_100_tail.tar.gz") - endif() - #resnet50 int8 + ### Image classification tests + set(IMAGENET_DATA_PATH "${INT8_DATA_DIR}/data.bin") + set(INT8_IMG_CLASS_TEST_APP "test_analyzer_int8_image_classification") + set(INT8_IMG_CLASS_TEST_APP_SRC "analyzer_int8_image_classification_tester.cc") + + # download dataset if necessary + download_int8_data(${INT8_DATA_DIR} "imagenet_val_100_tail.tar.gz") + + # build test binary to be used in subsequent tests + inference_analysis_api_int8_test_build(${INT8_IMG_CLASS_TEST_APP} ${INT8_IMG_CLASS_TEST_APP_SRC}) + + # resnet50 int8 set(INT8_RESNET50_MODEL_DIR "${INT8_DATA_DIR}/resnet50") - if (NOT EXISTS ${INT8_RESNET50_MODEL_DIR}) - inference_download_and_uncompress(${INT8_RESNET50_MODEL_DIR} "${INFERENCE_URL}/int8" "resnet50_int8_model.tar.gz" ) - endif() - inference_analysis_api_int8_test(test_analyzer_int8_resnet50 ${INT8_RESNET50_MODEL_DIR} ${INT8_DATA_DIR} analyzer_int8_image_classification_tester.cc SERIAL) - - #mobilenet int8 - set(INT8_MOBILENET_MODEL_DIR "${INT8_DATA_DIR}/mobilenet") - if (NOT EXISTS ${INT8_MOBILENET_MODEL_DIR}) - inference_download_and_uncompress(${INT8_MOBILENET_MODEL_DIR} "${INFERENCE_URL}/int8" "mobilenetv1_int8_model.tar.gz" ) - endif() - inference_analysis_api_int8_test(test_analyzer_int8_mobilenet ${INT8_MOBILENET_MODEL_DIR} ${INT8_DATA_DIR} analyzer_int8_image_classification_tester.cc SERIAL) + download_int8_data(${INT8_RESNET50_MODEL_DIR} "resnet50_int8_model.tar.gz" ) + inference_analysis_api_int8_test_run(test_analyzer_int8_resnet50 ${INT8_IMG_CLASS_TEST_APP} ${INT8_RESNET50_MODEL_DIR} ${IMAGENET_DATA_PATH}) + + # mobilenetv1 int8 + set(INT8_MOBILENETV1_MODEL_DIR "${INT8_DATA_DIR}/mobilenetv1") + download_int8_data(${INT8_MOBILENETV1_MODEL_DIR} "mobilenetv1_int8_model.tar.gz" ) + inference_analysis_api_int8_test_run(test_analyzer_int8_mobilenetv1 ${INT8_IMG_CLASS_TEST_APP} ${INT8_MOBILENETV1_MODEL_DIR} ${IMAGENET_DATA_PATH}) + + # mobilenetv2 int8 + set(INT8_MOBILENETV2_MODEL_DIR "${INT8_DATA_DIR}/mobilenetv2") + download_int8_data(${INT8_MOBILENETV2_MODEL_DIR} "mobilenet_v2_int8_model.tar.gz" ) + inference_analysis_api_int8_test_run(test_analyzer_int8_mobilenetv2 ${INT8_IMG_CLASS_TEST_APP} ${INT8_MOBILENETV2_MODEL_DIR} ${IMAGENET_DATA_PATH}) + + # resnet101 int8 + set(INT8_RESNET101_MODEL_DIR "${INT8_DATA_DIR}/resnet101") + download_int8_data(${INT8_RESNET101_MODEL_DIR} "Res101_int8_model.tar.gz" ) + inference_analysis_api_int8_test_run(test_analyzer_int8_resnet101 ${INT8_IMG_CLASS_TEST_APP} ${INT8_RESNET101_MODEL_DIR} ${IMAGENET_DATA_PATH}) + + # vgg16 int8 + set(INT8_VGG16_MODEL_DIR "${INT8_DATA_DIR}/vgg16") + download_int8_data(${INT8_VGG16_MODEL_DIR} "VGG16_int8_model.tar.gz" ) + inference_analysis_api_int8_test_run(test_analyzer_int8_vgg16 ${INT8_IMG_CLASS_TEST_APP} ${INT8_VGG16_MODEL_DIR} ${IMAGENET_DATA_PATH}) + + # vgg19 int8 + set(INT8_VGG19_MODEL_DIR "${INT8_DATA_DIR}/vgg19") + download_int8_data(${INT8_VGG19_MODEL_DIR} "VGG19_int8_model.tar.gz" ) + inference_analysis_api_int8_test_run(test_analyzer_int8_vgg19 ${INT8_IMG_CLASS_TEST_APP} ${INT8_VGG19_MODEL_DIR} ${IMAGENET_DATA_PATH}) + + # googlenet int8 + set(INT8_GOOGLENET_MODEL_DIR "${INT8_DATA_DIR}/googlenet") + download_int8_data(${INT8_GOOGLENET_MODEL_DIR} "GoogleNet_int8_model.tar.gz" ) + inference_analysis_api_int8_test_run(test_analyzer_int8_googlenet ${INT8_IMG_CLASS_TEST_APP} ${INT8_GOOGLENET_MODEL_DIR} ${IMAGENET_DATA_PATH}) + + ### Object detection models + set(PASCALVOC_DATA_PATH "${INT8_DATA_DIR}/pascalvoc_data.bin") + set(INT8_OBJ_DETECT_TEST_APP "test_analyzer_int8_object_detection") + set(INT8_OBJ_DETECT_TEST_APP_SRC "analyzer_int8_object_detection_tester.cc") + + # download dataset if necessary + download_int8_data(${INT8_DATA_DIR} "pascalvoc_val_100_head.tar.gz") + + # build test binary to be used in subsequent tests + inference_analysis_api_int8_test_build(${INT8_OBJ_DETECT_TEST_APP} ${INT8_OBJ_DETECT_TEST_APP_SRC}) + + # mobilenet-ssd int8 + set(INT8_MOBILENET_SSD_MODEL_DIR "${INT8_DATA_DIR}/mobilenet-ssd") + download_int8_data(${INT8_MOBILENET_SSD_MODEL_DIR} "mobilenet_ssd_int8_model.tar.gz" ) + inference_analysis_api_int8_test_run(test_analyzer_int8_mobilenet_ssd ${INT8_OBJ_DETECT_TEST_APP} ${INT8_MOBILENET_SSD_MODEL_DIR} ${PASCALVOC_DATA_PATH}) + endif() # bert, max_len=20, embedding_dim=128 set(BERT_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/bert_emb128") download_model_and_data(${BERT_INSTALL_DIR} "bert_emb128_model.tar.gz" "bert_data_len20.txt.tar.gz") -inference_analysis_api_test(test_analyzer_bert ${BERT_INSTALL_DIR} analyzer_bert_tester.cc SERIAL) +inference_analysis_api_test(test_analyzer_bert ${BERT_INSTALL_DIR} analyzer_bert_tester.cc) # anakin -if (WITH_ANAKIN AND WITH_MKL) # only needed in CI +if (ANAKIN_FOUND AND WITH_MKL) # only needed in CI # anakin rnn1 set(ANAKIN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/anakin") set(ANAKIN_RNN1_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/rnn1") @@ -190,14 +266,14 @@ if (WITH_ANAKIN AND WITH_MKL) # only needed in CI cc_test(test_anakin_rnn1 SRCS anakin_rnn1_tester.cc ARGS --model=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn.anakin2.model.bin --datapath=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn_data.txt - DEPS inference_anakin_api_shared SERIAL) + DEPS inference_anakin_api_shared) # anakin mobilenet if(WITH_GPU) set(ANAKIN_MOBILENET_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/mobilenet") inference_download(${ANAKIN_MOBILENET_INSTALL_DIR} ${INFERENCE_URL} "mobilenet_v2.anakin.bin") cc_test(test_anakin_mobilenet SRCS anakin_mobilenet_tester.cc ARGS --model=${ANAKIN_MOBILENET_INSTALL_DIR}/mobilenet_v2.anakin.bin - DEPS inference_anakin_api_shared dynload_cuda SERIAL) + DEPS inference_anakin_api_shared dynload_cuda) endif() endif() @@ -206,7 +282,13 @@ if(WITH_GPU AND TENSORRT_FOUND) if (NOT EXISTS ${TRT_MODEL_INSTALL_DIR}) inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz") endif() - inference_analysis_test(test_trt_models SRCS trt_models_tester.cc + inference_analysis_test(trt_mobilenet_test SRCS trt_mobilenet_test.cc + EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} + ARGS --infer_model=${TRT_MODEL_INSTALL_DIR}/trt_test_models) + inference_analysis_test(trt_resnet50_test SRCS trt_resnet50_test.cc + EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} + ARGS --infer_model=${TRT_MODEL_INSTALL_DIR}/trt_test_models) + inference_analysis_test(trt_resnext_test SRCS trt_resnext_test.cc EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} - ARGS --infer_model=${TRT_MODEL_INSTALL_DIR}/trt_test_models SERIAL) + ARGS --infer_model=${TRT_MODEL_INSTALL_DIR}/trt_test_models) endif() diff --git a/paddle/fluid/inference/tests/api/anakin_mobilenet_tester.cc b/paddle/fluid/inference/tests/api/anakin_mobilenet_tester.cc index cf97f064beddb6ede1d4716f323b4c5b46cb266d..48689486af4fc8aa7a10c67737393cf4e3d91d6b 100644 --- a/paddle/fluid/inference/tests/api/anakin_mobilenet_tester.cc +++ b/paddle/fluid/inference/tests/api/anakin_mobilenet_tester.cc @@ -27,8 +27,8 @@ contrib::AnakinConfig GetConfig() { // using AnakinConfig::X86 if you need to use cpu to do inference config.target_type = contrib::AnakinConfig::NVGPU; config.model_file = FLAGS_model; - config.device = 0; - config.max_batch_size = 1; + config.device_id = 0; + config.init_batch_size = 1; return config; } diff --git a/paddle/fluid/inference/tests/api/anakin_rnn1_tester.cc b/paddle/fluid/inference/tests/api/anakin_rnn1_tester.cc index da42688f29f044639d6d97ff09f932490d4c3d54..db01cfebcb2b303828147022306a222d3bc1ff21 100644 --- a/paddle/fluid/inference/tests/api/anakin_rnn1_tester.cc +++ b/paddle/fluid/inference/tests/api/anakin_rnn1_tester.cc @@ -100,8 +100,8 @@ contrib::AnakinConfig GetConfig() { // using AnakinConfig::X86 if you need to use cpu to do inference config.target_type = contrib::AnakinConfig::X86; config.model_file = FLAGS_model; - config.device = 0; - config.max_batch_size = 1000; // the max number of token + config.device_id = 0; + config.init_batch_size = 1000; // the max number of token return config; } diff --git a/paddle/fluid/inference/tests/api/analyzer_bert_tester.cc b/paddle/fluid/inference/tests/api/analyzer_bert_tester.cc index 9b2e74ec16eb3b6e98bfcc8cc546ed74a7966f33..45256234b83b804967cf3605fbc2acd6d3cc5ac6 100644 --- a/paddle/fluid/inference/tests/api/analyzer_bert_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_bert_tester.cc @@ -146,12 +146,17 @@ bool LoadInputData(std::vector> *inputs) { void SetConfig(AnalysisConfig *config) { config->SetModel(FLAGS_infer_model); } -void profile(bool use_mkldnn = false) { +void profile(bool use_mkldnn = false, bool use_ngraph = false) { AnalysisConfig config; SetConfig(&config); if (use_mkldnn) { config.EnableMKLDNN(); + config.pass_builder()->AppendPass("fc_mkldnn_pass"); + } + + if (use_ngraph) { + config.EnableNgraph(); } std::vector> outputs; @@ -163,7 +168,11 @@ void profile(bool use_mkldnn = false) { TEST(Analyzer_bert, profile) { profile(); } #ifdef PADDLE_WITH_MKLDNN -TEST(Analyzer_bert, profile_mkldnn) { profile(true); } +TEST(Analyzer_bert, profile_mkldnn) { profile(true, false); } +#endif + +#ifdef PADDLE_WITH_NGRAPH +TEST(Analyzer_bert, profile_ngraph) { profile(false, true); } #endif // Check the fuse status @@ -178,11 +187,16 @@ TEST(Analyzer_bert, fuse_statis) { } // Compare result of NativeConfig and AnalysisConfig -void compare(bool use_mkldnn = false) { +void compare(bool use_mkldnn = false, bool use_ngraph = false) { AnalysisConfig cfg; SetConfig(&cfg); if (use_mkldnn) { cfg.EnableMKLDNN(); + cfg.pass_builder()->AppendPass("fc_mkldnn_pass"); + } + + if (use_ngraph) { + cfg.EnableNgraph(); } std::vector> inputs; @@ -193,7 +207,15 @@ void compare(bool use_mkldnn = false) { TEST(Analyzer_bert, compare) { compare(); } #ifdef PADDLE_WITH_MKLDNN -TEST(Analyzer_bert, compare_mkldnn) { compare(true /* use_mkldnn */); } +TEST(Analyzer_bert, compare_mkldnn) { + compare(true, false /* use_mkldnn, no use_ngraph */); +} +#endif + +#ifdef PADDLE_WITH_NGRAPH +TEST(Analyzer_bert, compare_ngraph) { + compare(false, true /* no use_mkldnn, use_ngraph */); +} #endif // Compare Deterministic result diff --git a/paddle/fluid/inference/tests/api/analyzer_dam_tester.cc b/paddle/fluid/inference/tests/api/analyzer_dam_tester.cc index a3eac7b200c37b4500183eb3888582d1dc695bb7..83bf99ec8aa2fe37e527ae4f7dcb89bed31bf779 100644 --- a/paddle/fluid/inference/tests/api/analyzer_dam_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_dam_tester.cc @@ -200,8 +200,9 @@ void profile(bool use_mkldnn = false) { cfg.EnableMKLDNN(); // Enable all the mkldnn supported ops except conv3d in dam std::unordered_set op_list = {"softmax", "elementwise_add", - "relu"}; + "relu", "fc"}; cfg.SetMKLDNNOp(op_list); + cfg.pass_builder()->AppendPass("fc_mkldnn_pass"); } std::vector> outputs; @@ -251,6 +252,7 @@ void compare(bool use_mkldnn = false) { std::unordered_set op_list = {"softmax", "elementwise_add", "relu"}; cfg.SetMKLDNNOp(op_list); + cfg.pass_builder()->AppendPass("fc_mkldnn_pass"); } std::vector> input_slots_all; @@ -321,7 +323,6 @@ TEST(Analyzer_dam, compare_determine) { CompareDeterministic(reinterpret_cast(&cfg), input_slots_all); } - // Save optim model TEST(Analyzer_dam, save_optim_model) { AnalysisConfig cfg; diff --git a/paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc b/paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc index 2eb347a44b394a55706d5aa88bee7fe1fcc7838e..17c670a68cc9cbcfd74ff3541fa1f3bc07200062 100644 --- a/paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc @@ -100,6 +100,7 @@ void profile(bool use_mkldnn = false) { if (use_mkldnn) { cfg.EnableMKLDNN(); + cfg.pass_builder()->AppendPass("fc_mkldnn_pass"); } std::vector> input_slots_all; @@ -146,6 +147,7 @@ void compare(bool use_mkldnn = false) { if (use_mkldnn) { cfg.EnableMKLDNN(); + cfg.pass_builder()->AppendPass("fc_mkldnn_pass"); } std::vector> input_slots_all; diff --git a/paddle/fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc b/paddle/fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc index cc31ab9588da01679b45c2bd4215f5eebd8447d1..11a49ed2914ae22c2ddb4cfe384900adfce4f21d 100644 --- a/paddle/fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc @@ -177,11 +177,15 @@ TEST(Analyzer_Pyramid_DNN, compare_zero_copy) { AnalysisConfig cfg; SetConfig(&cfg); + AnalysisConfig cfg1; + SetConfig(&cfg1); + std::vector> input_slots_all; SetInput(&input_slots_all); std::vector outputs_name; outputs_name.emplace_back("cos_sim_2.tmp_0"); CompareAnalysisAndZeroCopy(reinterpret_cast(&cfg), + reinterpret_cast(&cfg1), input_slots_all, outputs_name); } diff --git a/paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc b/paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc index 54fd3a4a4caba52110ab636e6d44ee2a473f0cb0..620a1d1f7a390f38fe2662169f35994dca9976f9 100644 --- a/paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc @@ -293,11 +293,15 @@ TEST(Analyzer_rnn1, compare_zero_copy) { AnalysisConfig cfg; SetConfig(&cfg); + AnalysisConfig cfg1; + SetConfig(&cfg1); + std::vector> input_slots_all; SetInput(&input_slots_all); std::vector outputs_name; outputs_name.emplace_back("final_output.tmp_1"); CompareAnalysisAndZeroCopy(reinterpret_cast(&cfg), + reinterpret_cast(&cfg1), input_slots_all, outputs_name); } diff --git a/paddle/fluid/inference/tests/api/analyzer_save_model_tester.cc b/paddle/fluid/inference/tests/api/analyzer_save_model_tester.cc index 578b420ea924754999640925a6b5f3fe524d7668..977b2ec885dcba8677a0705f698cd0200b789916 100644 --- a/paddle/fluid/inference/tests/api/analyzer_save_model_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_save_model_tester.cc @@ -34,14 +34,22 @@ TEST(Analyzer, save_model) { AnalysisConfig cfg; SetConfig(&cfg); cfg.SetModel(FLAGS_infer_model + "/__model__", FLAGS_infer_model + "/param"); - std::string optimModelPath = FLAGS_infer_model + "/saved_optim_model"; + // ensure the path being unique + std::string optimModelPath = FLAGS_infer_model + "/only_for_save_model_test"; mkdir(optimModelPath.c_str(), 0777); SaveOptimModel(&cfg, optimModelPath); - cfg.pass_builder()->ClearPasses(); - int origin_num_ops = GetNumOps(cfg); - cfg.SetModel(optimModelPath + "/model", optimModelPath + "/params"); - int fused_num_ops = GetNumOps(cfg); + // Each config can only be applied to one predictor. + AnalysisConfig cfg2; + SetConfig(&cfg2); + cfg2.pass_builder()->ClearPasses(); + cfg2.SetModel(optimModelPath + "/model", optimModelPath + "/params"); + int origin_num_ops = GetNumOps(cfg2); + + AnalysisConfig cfg3; + SetConfig(&cfg3); + cfg3.SetModel(optimModelPath + "/model", optimModelPath + "/params"); + int fused_num_ops = GetNumOps(cfg3); CHECK_LE(fused_num_ops, origin_num_ops); } diff --git a/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc b/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc index 3cebf8e96984fad0de8d8c6775990f7c6a6cabe5..e6f2bfad68c9883b50f7fdd306a65946c178e50a 100644 --- a/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc @@ -149,6 +149,7 @@ void SetConfig(AnalysisConfig *cfg, bool use_mkldnn = false) { } if (use_mkldnn) { cfg->EnableMKLDNN(); + cfg->pass_builder()->AppendPass("fc_mkldnn_pass"); } // Enable seqpool_concat_fuse_pass, disabled by default since it takes much // time @@ -214,11 +215,15 @@ TEST(Analyzer_seq_pool1, compare_zero_copy) { AnalysisConfig cfg; SetConfig(&cfg); + AnalysisConfig cfg1; + SetConfig(&cfg1); + std::vector> input_slots_all; SetInput(&input_slots_all); std::vector outputs_name; outputs_name.emplace_back(out_var_name); CompareAnalysisAndZeroCopy(reinterpret_cast(&cfg), + reinterpret_cast(&cfg1), input_slots_all, outputs_name); } diff --git a/paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc b/paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc index 54492dbc238bbaf25f86b300fdd6585f74365088..78e500b2ed530d5a1dce8a7927538fdd0bbb6907 100644 --- a/paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc @@ -36,6 +36,8 @@ struct DataReader { tensor.lod.front().push_back(data.size()); tensor.data.Resize(data.size() * sizeof(int64_t)); + CHECK(tensor.data.data() != nullptr); + CHECK(data.data() != nullptr); memcpy(tensor.data.data(), data.data(), data.size() * sizeof(int64_t)); tensor.shape.push_back(data.size()); tensor.shape.push_back(1); @@ -87,7 +89,12 @@ TEST(Analyzer_Text_Classification, profile) { CHECK_EQ(output.lod.size(), 0UL); LOG(INFO) << "output.dtype: " << output.dtype; std::stringstream ss; - for (int i = 0; i < 5; i++) { + int num_data = 1; + for (auto i : output.shape) { + num_data *= i; + } + + for (int i = 0; i < num_data; i++) { ss << static_cast(output.data.data())[i] << " "; } LOG(INFO) << "output.data summary: " << ss.str(); diff --git a/paddle/fluid/inference/tests/api/analyzer_transformer_tester.cc b/paddle/fluid/inference/tests/api/analyzer_transformer_tester.cc index a23297f29cf65d891f530850ffd184aa58e10886..f2195966add8c4c159d26682c9578c95301a345f 100644 --- a/paddle/fluid/inference/tests/api/analyzer_transformer_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_transformer_tester.cc @@ -189,6 +189,7 @@ void profile(bool use_mkldnn = false) { std::vector> outputs; if (use_mkldnn) { cfg.EnableMKLDNN(); + cfg.pass_builder()->AppendPass("fc_mkldnn_pass"); } std::vector> input_slots_all; @@ -219,6 +220,7 @@ void compare(bool use_mkldnn = false) { SetConfig(&cfg); if (use_mkldnn) { cfg.EnableMKLDNN(); + cfg.pass_builder()->AppendPass("fc_mkldnn_pass"); } std::vector> input_slots_all; diff --git a/paddle/fluid/inference/tests/api/analyzer_vis_tester.cc b/paddle/fluid/inference/tests/api/analyzer_vis_tester.cc index fb47048cd0ccc887927cb4b533d45df11ef633eb..5f65229ecd52abb904654647eb2f00a8248d8632 100644 --- a/paddle/fluid/inference/tests/api/analyzer_vis_tester.cc +++ b/paddle/fluid/inference/tests/api/analyzer_vis_tester.cc @@ -85,6 +85,7 @@ void profile(bool use_mkldnn = false) { SetConfig(&cfg); if (use_mkldnn) { cfg.EnableMKLDNN(); + cfg.pass_builder()->AppendPass("fc_mkldnn_pass"); } // cfg.pass_builder()->TurnOnDebug(); std::vector> outputs; @@ -132,6 +133,7 @@ void compare(bool use_mkldnn = false) { SetConfig(&cfg); if (use_mkldnn) { cfg.EnableMKLDNN(); + cfg.pass_builder()->AppendPass("fc_mkldnn_pass"); } std::vector> input_slots_all; diff --git a/paddle/fluid/inference/tests/api/config_printer.h b/paddle/fluid/inference/tests/api/config_printer.h index b952b62f13ed6c1b6bd0b90bdc5898e9b8ef6f20..de938669c0b0866f9c2f55cd04b866f9a9611294 100644 --- a/paddle/fluid/inference/tests/api/config_printer.h +++ b/paddle/fluid/inference/tests/api/config_printer.h @@ -78,6 +78,8 @@ std::ostream &operator<<(std::ostream &os, const AnalysisConfig &config) { << "use_tensorrt: " << config.tensorrt_engine_enabled() << "\n"; os << GenSpaces(num_spaces) << "use_mkldnn: " << config.mkldnn_enabled() << "\n"; + os << GenSpaces(num_spaces) << "use_ngraph: " << config.ngraph_enabled() + << "\n"; num_spaces--; os << GenSpaces(num_spaces) << "}\n"; return os; diff --git a/paddle/fluid/inference/tests/api/tester_helper.h b/paddle/fluid/inference/tests/api/tester_helper.h index a50810948ff8cb9e0bb92c287a7ab3945d39e089..eb786196a88482817617f0156327be95e67bd4ad 100644 --- a/paddle/fluid/inference/tests/api/tester_helper.h +++ b/paddle/fluid/inference/tests/api/tester_helper.h @@ -148,7 +148,7 @@ void CompareResult(const std::vector &outputs, case PaddleDType::INT64: { int64_t *pdata = static_cast(out.data.data()); int64_t *pdata_ref = ref_out.data(&place, &ref_size); - EXPECT_EQ(size, ref_size); + EXPECT_EQ(size, static_cast(ref_size)); for (size_t j = 0; j < size; ++j) { EXPECT_EQ(pdata_ref[j], pdata[j]); } @@ -320,7 +320,8 @@ void PredictionRun(PaddlePredictor *predictor, const std::vector> &inputs, std::vector> *outputs, int num_threads, int tid, - const VarType::Type data_type = VarType::FP32) { + const VarType::Type data_type = VarType::FP32, + float *sample_latency = nullptr) { int num_times = FLAGS_repeat; int iterations = inputs.size(); // process the whole dataset ... if (FLAGS_iterations > 0 && @@ -360,6 +361,10 @@ void PredictionRun(PaddlePredictor *predictor, auto batch_latency = elapsed_time / (iterations * num_times); PrintTime(FLAGS_batch_size, num_times, num_threads, tid, batch_latency, iterations, data_type); + + if (sample_latency != nullptr) + *sample_latency = batch_latency / FLAGS_batch_size; + if (FLAGS_record_benchmark) { Benchmark benchmark; benchmark.SetName(FLAGS_model_name); @@ -373,12 +378,14 @@ void TestOneThreadPrediction( const PaddlePredictor::Config *config, const std::vector> &inputs, std::vector> *outputs, bool use_analysis = true, - const VarType::Type data_type = VarType::FP32) { + const VarType::Type data_type = VarType::FP32, + float *sample_latency = nullptr) { auto predictor = CreateTestPredictor(config, use_analysis); if (FLAGS_warmup) { PredictionWarmUp(predictor.get(), inputs, outputs, 1, 0, data_type); } - PredictionRun(predictor.get(), inputs, outputs, 1, 0, data_type); + PredictionRun(predictor.get(), inputs, outputs, 1, 0, data_type, + sample_latency); } void TestMultiThreadPrediction( @@ -430,6 +437,31 @@ void TestPrediction(const PaddlePredictor::Config *config, } } +void SummarizeAccuracy(float avg_acc1_fp32, float avg_acc1_int8) { + LOG(INFO) << "--- Accuracy summary --- "; + LOG(INFO) << "Accepted top1 accuracy drop threshold: " + << FLAGS_quantized_accuracy + << ". (condition: (FP32_top1_acc - INT8_top1_acc) <= threshold)"; + LOG(INFO) << "FP32: avg top1 accuracy: " << std::fixed << std::setw(6) + << std::setprecision(4) << avg_acc1_fp32; + LOG(INFO) << "INT8: avg top1 accuracy: " << std::fixed << std::setw(6) + << std::setprecision(4) << avg_acc1_int8; +} + +void SummarizePerformance(float sample_latency_fp32, + float sample_latency_int8) { + // sample latency in ms + auto throughput_fp32 = 1000.0 / sample_latency_fp32; + auto throughput_int8 = 1000.0 / sample_latency_int8; + LOG(INFO) << "--- Performance summary --- "; + LOG(INFO) << "FP32: avg fps: " << std::fixed << std::setw(6) + << std::setprecision(4) << throughput_fp32 + << ", avg latency: " << sample_latency_fp32 << " ms"; + LOG(INFO) << "INT8: avg fps: " << std::fixed << std::setw(6) + << std::setprecision(4) << throughput_int8 + << ", avg latency: " << sample_latency_int8 << " ms"; +} + void CompareTopAccuracy( const std::vector> &output_slots_quant, const std::vector> &output_slots_ref) { @@ -459,12 +491,10 @@ void CompareTopAccuracy( float avg_acc1_quant = total_accs1_quant / output_slots_quant.size(); float avg_acc1_ref = total_accs1_ref / output_slots_ref.size(); - LOG(INFO) << "Avg top1 INT8 accuracy: " << std::fixed << std::setw(6) - << std::setprecision(4) << avg_acc1_quant; - LOG(INFO) << "Avg top1 FP32 accuracy: " << std::fixed << std::setw(6) - << std::setprecision(4) << avg_acc1_ref; - LOG(INFO) << "Accepted accuracy drop threshold: " << FLAGS_quantized_accuracy; - CHECK_LE(std::abs(avg_acc1_quant - avg_acc1_ref), FLAGS_quantized_accuracy); + SummarizeAccuracy(avg_acc1_ref, avg_acc1_quant); + CHECK_GT(avg_acc1_ref, 0.0); + CHECK_GT(avg_acc1_quant, 0.0); + CHECK_LE(avg_acc1_ref - avg_acc1_quant, FLAGS_quantized_accuracy); } void CompareDeterministic( @@ -510,16 +540,19 @@ void CompareQuantizedAndAnalysis( auto *cfg = reinterpret_cast(config); PrintConfig(cfg, true); std::vector> analysis_outputs; - TestOneThreadPrediction(cfg, inputs, &analysis_outputs, true, VarType::FP32); + float sample_latency_fp32{-1}; + TestOneThreadPrediction(cfg, inputs, &analysis_outputs, true, VarType::FP32, + &sample_latency_fp32); LOG(INFO) << "--- INT8 prediction start ---"; auto *qcfg = reinterpret_cast(qconfig); PrintConfig(qcfg, true); std::vector> quantized_outputs; - TestOneThreadPrediction(qcfg, inputs, &quantized_outputs, true, - VarType::INT8); + float sample_latency_int8{-1}; + TestOneThreadPrediction(qcfg, inputs, &quantized_outputs, true, VarType::INT8, + &sample_latency_int8); - LOG(INFO) << "--- comparing outputs --- "; + SummarizePerformance(sample_latency_fp32, sample_latency_int8); CompareTopAccuracy(quantized_outputs, analysis_outputs); } @@ -534,7 +567,7 @@ void CompareNativeAndAnalysis( } void CompareAnalysisAndZeroCopy( - PaddlePredictor::Config *config, + PaddlePredictor::Config *config, PaddlePredictor::Config *config1, const std::vector> &inputs, const std::vector &outputs_name) { int batch_size = FLAGS_batch_size; @@ -544,8 +577,8 @@ void CompareAnalysisAndZeroCopy( predictor->Run(inputs[0], &analysis_outputs, batch_size); // analysis + zero_copy std::vector zerocopy_outputs; - reinterpret_cast(config)->SwitchUseFeedFetchOps(false); - predictor = CreateTestPredictor(config, true); + reinterpret_cast(config1)->SwitchUseFeedFetchOps(false); + predictor = CreateTestPredictor(config1, true); ConvertPaddleTensorToZeroCopyTensor(predictor.get(), inputs[0]); predictor->ZeroCopyRun(); for (size_t i = 0; i < outputs_name.size(); i++) { diff --git a/paddle/fluid/inference/tests/test.cmake b/paddle/fluid/inference/tests/test.cmake index c93c9ef2f2337124da349517ad13b27acb10b2c1..444bab1b33df063221828fe6c1457e2af672e652 100644 --- a/paddle/fluid/inference/tests/test.cmake +++ b/paddle/fluid/inference/tests/test.cmake @@ -48,13 +48,35 @@ if(NOT EXISTS ${WORD2VEC_INSTALL_DIR} AND NOT WIN32) endif() set(WORD2VEC_MODEL_DIR "${WORD2VEC_INSTALL_DIR}/word2vec.inference.model") -function (inference_base_test TARGET) +function (inference_base_test_build TARGET) set(options "") set(oneValueArgs "") - set(multiValueArgs SRCS ARGS DEPS) + set(multiValueArgs SRCS DEPS) + cmake_parse_arguments(base_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + cc_test_build(${TARGET} SRCS ${base_test_SRCS} DEPS ${base_test_DEPS}) +endfunction() + +function (inference_base_test_run TARGET) + set(options "") + set(oneValueArgs "") + set(multiValueArgs COMMAND ARGS) cmake_parse_arguments(base_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) if(WITH_GPU) set(mem_opt "--fraction_of_gpu_memory_to_use=0.5") endif() - cc_test(${TARGET} SRCS ${base_test_SRCS} DEPS ${base_test_DEPS} ARGS ${mem_opt} ${base_test_ARGS}) + cc_test_run(${TARGET} COMMAND ${base_test_COMMAND} ARGS ${mem_opt} ${base_test_ARGS}) endfunction() + +function (inference_base_test TARGET) + set(options "") + set(oneValueArgs "") + set(multiValueArgs SRCS ARGS DEPS) + cmake_parse_arguments(base_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + inference_base_test_build(${TARGET} + SRCS ${base_test_SRCS} + DEPS ${base_test_DEPS}) + inference_base_test_run(${TARGET} + COMMAND ${TARGET} + ARGS ${base_test_ARGS}) +endfunction() + diff --git a/paddle/fluid/memory/CMakeLists.txt b/paddle/fluid/memory/CMakeLists.txt index 0d4c5c37e1dc88ec8ea42070db561ee25b813ba9..7eb663ea280e65f3c10304aa47c9970df099b901 100644 --- a/paddle/fluid/memory/CMakeLists.txt +++ b/paddle/fluid/memory/CMakeLists.txt @@ -6,8 +6,7 @@ cc_library(memcpy SRCS memcpy.cc DEPS place) cc_library(memory DEPS malloc - memcpy - ) + memcpy) #if (WITH_GPU) # nv_test(pinned_memory_test SRCS pinned_memory_test.cu DEPS place memory) #endif() diff --git a/paddle/fluid/memory/allocation/CMakeLists.txt b/paddle/fluid/memory/allocation/CMakeLists.txt index 3dbbea3dd0beb78997f6e8f6b7451ea806bce9b3..c309febd49905104c259d71f5c56bf58b7294090 100644 --- a/paddle/fluid/memory/allocation/CMakeLists.txt +++ b/paddle/fluid/memory/allocation/CMakeLists.txt @@ -37,30 +37,19 @@ else () set(AllocatorFacadeDeps) endif() +list(APPEND AllocatorFacadeDeps cpu_allocator locked_allocator best_fit_allocator aligned_allocator auto_increment_allocator conditional_allocator retry_allocator buffered_allocator legacy_allocator) + cc_library(aligned_allocator SRCS aligned_allocator.cc DEPS allocator) cc_library(auto_increment_allocator SRCS auto_increment_allocator.cc DEPS allocator) -cc_library(zero_size_allocator SRCS zero_size_allocator.cc DEPS allocator) cc_library(conditional_allocator SRCS conditional_allocator.cc DEPS allocator) -cc_library(allocator_strategy SRCS allocator_strategy.cc DEPS gflags) -cc_library(allocator_facade SRCS allocator_facade.cc DEPS - ${AllocatorFacadeDeps} - cpu_allocator - locked_allocator - best_fit_allocator - aligned_allocator - auto_increment_allocator - zero_size_allocator - conditional_allocator - retry_allocator - buffered_allocator - allocator_strategy - legacy_allocator - ) +cc_library(allocator_strategy SRCS allocator_strategy.cc DEPS gflags ${AllocatorFacadeDeps}) +cc_library(allocator_facade SRCS allocator_facade.cc DEPS allocator_strategy) nv_test(allocation_and_eigen_test SRCS allocation_and_eigen_test.cu DEPS allocator_facade) -cc_test(retry_allocator_test SRCS retry_allocator_test.cc DEPS retry_allocator best_fit_allocator locked_allocator cpu_allocator) +cc_test(naive_best_fit_allocator_facade_test SRCS naive_best_fit_allocator_facade_test.cc DEPS allocator_facade) +cc_test(retry_allocator_test SRCS retry_allocator_test.cc DEPS retry_allocator best_fit_allocator locked_allocator cpu_allocator) if (WITH_TESTING) set_tests_properties(retry_allocator_test PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE") endif() diff --git a/paddle/fluid/memory/allocation/aligned_allocator.h b/paddle/fluid/memory/allocation/aligned_allocator.h index 064acd06e71da98802126913e0af843cfbf717e7..7cedad3d66c8e68d7fe319fd6d27f074b924cbd6 100644 --- a/paddle/fluid/memory/allocation/aligned_allocator.h +++ b/paddle/fluid/memory/allocation/aligned_allocator.h @@ -89,11 +89,12 @@ class AlignedAllocator : public ThinAlignedAllocator { using ThinAlignedAllocator::ThinAlignedAllocator; protected: - Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override { - auto raw_allocation = - underlying_allocator_->Allocate(size + kAlignment, attr); + Allocation* AllocateImpl(size_t size) override { + auto raw_allocation = underlying_allocator_->Allocate(size + kAlignment); return new AlignedAllocation(std::move(raw_allocation), size); } + + void FreeImpl(Allocation* allocation) override { delete allocation; } }; } // namespace allocation diff --git a/paddle/fluid/memory/allocation/allocator.cc b/paddle/fluid/memory/allocation/allocator.cc index 8fb8a5fb897a736d7515951ba08c633da9a7706c..4998f3dbb9613abbf5ca67a3d43863d01483b79f 100644 --- a/paddle/fluid/memory/allocation/allocator.cc +++ b/paddle/fluid/memory/allocation/allocator.cc @@ -14,29 +14,14 @@ #include "paddle/fluid/memory/allocation/allocator.h" -#include - namespace paddle { namespace memory { namespace allocation { -Allocation::~Allocation() {} - -Allocator::~Allocator() {} bool Allocator::IsAllocThreadSafe() const { return false; } -AllocationPtr Allocator::Allocate(size_t size, Allocator::Attr attr) { - auto ptr = AllocateImpl(size, attr); - ptr->set_allocator(this); - return AllocationPtr(ptr); -} - -void Allocator::Free(Allocation* allocation) { delete allocation; } - -const char* BadAlloc::what() const noexcept { return msg_.c_str(); } - -void AllocationDeleter::operator()(Allocation* allocation) const { - auto* allocator = allocation->allocator(); +void Allocator::FreeImpl(Allocation* allocation) { + Allocator* allocator = allocation->TopDecoratedAllocator(); allocator->Free(allocation); } diff --git a/paddle/fluid/memory/allocation/allocator.h b/paddle/fluid/memory/allocation/allocator.h index 3465278935f7ce05456e94bb3a7d1ae9f114ff96..d31f37268d96af529801fe0fa1b28ec6e80aed76 100644 --- a/paddle/fluid/memory/allocation/allocator.h +++ b/paddle/fluid/memory/allocation/allocator.h @@ -15,8 +15,10 @@ #pragma once #include #include +#include #include #include +#include "paddle/fluid/framework/inlined_vector.h" #include "paddle/fluid/platform/place.h" namespace paddle { @@ -26,40 +28,73 @@ namespace allocation { // Exception when `Alloc`/`AllocShared` failed class BadAlloc : public std::exception { public: - explicit BadAlloc(std::string msg) : msg_(std::move(msg)) {} - const char* what() const noexcept override; + inline explicit BadAlloc(std::string msg) : msg_(std::move(msg)) {} + + inline const char* what() const noexcept override { return msg_.c_str(); } private: std::string msg_; }; -class Allocation; -class AllocationDeleter { - public: - void operator()(Allocation* allocation) const; -}; - class Allocator; + // Allocation is the object holding the actually pointer. Use // `Allocation::ptr()` will returns the pointer that allocated. // // NOTE: this is the base class of Allocation. Each allocator can use its own // allocation object. // NOTE: the `Allocation::ptr()` could be nullptr, if the allocation size is 0 + +/** + * Allocation is returned by Allocator::Allocate() method. + * + * An allocator may be decorated by another allocator. For example, we can + * decorate a RetryAllocator to any allocator to perform allocation retry when + * first allocation request fails. + * + * Explanations of Allocator design are as follows: + * + * Suppose we have an allocator which is decorated by several allocators: + * + * A(1) <- A(2) <- A(3) <- ... <- A(n) + * + * , and the public allocator is A(1). + * + * The allocation process would be: + * + * A(n).Allocate() -> ... -> A(2).Allocate() -> A(1).Allocate() + * + * , and the free process would be: + * + * A(1).Free() -> A(2).Free() -> ... -> A(n).Free() + * + * Therefore, we should record the allocator chain when allocating, so + * that we can free the allocation in the reverse order of allocator chain. + * The field `decorated_allocators_` is used to record this chain. + * + * Another example is that we want to add additional fields in Allocation, + * e.g., something what is done in AlignedAllocator, etc. + * In this case, we should declare a derived class of Allocation, which + * contains an underlying Allocation allocated by the underlying allocator. + * Therefore, `decorated_allocators_` of the new Allocation object would + * be a new chain, differing from the underlying Allocation object. + */ class Allocation { public: - Allocation(void* ptr, size_t size, platform::Place place) - : allocator_(nullptr), ptr_(ptr), size_(size), place_(place) {} + inline Allocation(void* ptr, size_t size, platform::Place place) + : ptr_(ptr), size_(size), place_(place) {} Allocation(const Allocation& o) = delete; Allocation& operator=(const Allocation& o) = delete; + Allocation(Allocation&& o) = delete; + Allocation& operator=(Allocation&& o) = delete; // Returns the holding pointer. // NOTE: For performance consideration, it is better not to make this method // as a virtual method. If we want to implement a `defragmentation` later, // we might need to make `ptr_` field as a protected field, and add a virtual // method like `defragmentation` to change `ptr_`. - void* ptr() const { return ptr_; } + inline void* ptr() const { return ptr_; } // Returns the size of this memory buffer, i.e., ptr() + size() - 1 is the // last valid element. @@ -70,78 +105,85 @@ class Allocation { // The raw pointer might not aligned, so an offset might be added to raw // the pointer. The size of this allocation will be // `size + kAlignemnt - offset`. - size_t size() const { return size_; } + inline size_t size() const { return size_; } - const platform::Place& place() const { return place_; } + inline const platform::Place& place() const { return place_; } - Allocator* allocator() { return allocator_; } + virtual ~Allocation() {} - void set_allocator(Allocator* allocator) { allocator_ = allocator; } + private: + inline void RegisterDecoratedAllocator(Allocator* allocator) { + decorated_allocators_.emplace_back(allocator); + } - virtual ~Allocation(); + inline void PopDecoratedAllocator() { decorated_allocators_.pop_back(); } + + inline Allocator* TopDecoratedAllocator() { + return decorated_allocators_.back(); + } private: - Allocator* allocator_; void* ptr_; size_t size_; platform::Place place_; -}; -using AllocationPtr = std::unique_ptr; + /** + * NOTE(zjl): Since decorated_allocators_ is usually a small vector. + * We reserve a small buffer to it to prevent frequent heap allocation + * + * Instead, we can use a std::vector here, and reserve + * kReserveAllocatorNum in constructor of Allocation. + * But using std::vector would make ocr recognition model + * fail in CE. The train duration is 8% slower than KPI. + */ + static constexpr size_t kReserveAllocatorNum = 8; + using DecoratedAllocatorStack = + framework::InlinedVector; + + DecoratedAllocatorStack decorated_allocators_; + + friend class Allocator; +}; // Base interface class of memory Allocator. -// To allocate a memory, allocator needs two parameters: -// 1. size of bytes. -// 2. Attribute of memory. -// NOTE: the attribute of memory might be ignored if the allocator does not -// care it. class Allocator { public: - enum Attr { - kDefault = 0, // Default attribute. Uses the fast or stablest allocation - // algorithm. - - kFixedHuge = 1, // The allocation may not be freed until the program - // ends. e.g., `Parameters` and `Momentum`. - - kFluxHuge = 2, // The allocation may create and freed frequently and the - // allocation is considerable huge. Like `activations` - // and gradients. - - kScratchpad = - 3, // The `Scratchpad` memory is allocated and freed very soon, - // usually within an operator or aux memory. - // Like CUDNN workspace, AUX memory in batch norm, etc. - // - // https://en.wikipedia.org/wiki/Scratchpad_memory - - kCrossDevice = - 4, // The memory used cross-device memory copy/communication. - // For example: - // 1. it can use an `pinned` memory for CPU-GPU - // communication. - // 2. it can use an `registered` memory for RDMA - // communication. - - NumOfAttrs = 5 // The number of all attributes. It is used internally. + virtual ~Allocator() {} + + class AllocationDeleter { + public: + inline void operator()(Allocation* allocation) const { + Allocator* allocator = allocation->TopDecoratedAllocator(); + allocator->Free(allocation); + } }; - virtual ~Allocator(); + using AllocationPtr = std::unique_ptr; // Allocate an allocation. - AllocationPtr Allocate(size_t size, Allocator::Attr attr = kDefault); + inline AllocationPtr Allocate(size_t size) { + auto ptr = AllocateImpl(size); + ptr->RegisterDecoratedAllocator(this); + return AllocationPtr(ptr); + } + + // This function should not be called outside Allocator class + inline void Free(Allocation* allocation) { + allocation->PopDecoratedAllocator(); + FreeImpl(allocation); + } // True if the `Allocate` is thread safe. virtual bool IsAllocThreadSafe() const; protected: - virtual void Free(Allocation* allocation); - virtual Allocation* AllocateImpl(size_t size, Allocator::Attr attr) = 0; - - private: - friend class AllocationDeleter; + virtual Allocation* AllocateImpl(size_t size) = 0; + virtual void FreeImpl(Allocation* allocation); }; +using AllocationDeleter = Allocator::AllocationDeleter; +using AllocationPtr = Allocator::AllocationPtr; + } // namespace allocation } // namespace memory } // namespace paddle diff --git a/paddle/fluid/memory/allocation/allocator_facade.cc b/paddle/fluid/memory/allocation/allocator_facade.cc index a3b73e3ba31c89c2a94955b0fea64df4ab0ffc26..440b2475f1631ce5b0a1018ccd13849cc2568cd5 100644 --- a/paddle/fluid/memory/allocation/allocator_facade.cc +++ b/paddle/fluid/memory/allocation/allocator_facade.cc @@ -29,7 +29,6 @@ #include "paddle/fluid/memory/allocation/legacy_allocator.h" #include "paddle/fluid/memory/allocation/locked_allocator.h" #include "paddle/fluid/memory/allocation/retry_allocator.h" -#include "paddle/fluid/memory/allocation/zero_size_allocator.h" #include "paddle/fluid/platform/cpu_info.h" #include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/place.h" @@ -49,6 +48,17 @@ namespace paddle { namespace memory { namespace allocation { +static inline std::shared_ptr WrapRetryAllocator( + std::shared_ptr allocator, int64_t retry_time) { + if (retry_time > 0) { + auto* retry_allocator = + new RetryAllocator(std::move(allocator), retry_time); + allocator.reset(retry_allocator); + } + + return allocator; +} + // TODO(yy): Dirty code here. This class should be configurable in runtime. class CPUManagedAllocator : public Allocator { public: @@ -57,8 +67,8 @@ class CPUManagedAllocator : public Allocator { bool IsAllocThreadSafe() const override { return true; } protected: - Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override { - return normal_allocator_->Allocate(size, attr).release(); + Allocation* AllocateImpl(size_t size) override { + return normal_allocator_->Allocate(size).release(); } private: @@ -91,11 +101,10 @@ class ChunkedAllocator : public Allocator { auto* cond_allocator = new ConditionalAllocator(); cond_allocator - ->AddAllocator( - [this](size_t size, Attr attr) { return size < max_chunk_size_; }, - default_allocator_) + ->AddAllocator([this](size_t size) { return size < max_chunk_size_; }, + default_allocator_) .AddAllocator( - [](size_t size, Attr attr) { + [](size_t size) { return true; // default case }, raw_allocator_); @@ -112,14 +121,10 @@ class ChunkedAllocator : public Allocator { std::shared_ptr CreateAllocatorWithChunk() { chunks_.emplace_back(raw_allocator_->Allocate(max_chunk_size_)); auto* allocation = chunks_.back().get(); - std::unique_ptr allocator(new LockedAllocator( - std::unique_ptr(new BestFitAllocator(allocation)))); + std::shared_ptr allocator(new LockedAllocator( + std::shared_ptr(new BestFitAllocator(allocation)))); - if (retry_time_ > 0) { - auto* retry_allocator = - new RetryAllocator(std::move(allocator), retry_time_); - allocator.reset(retry_allocator); - } + allocator = WrapRetryAllocator(allocator, retry_time_); return std::make_shared>(std::move(allocator)); } @@ -127,8 +132,8 @@ class ChunkedAllocator : public Allocator { bool IsAllocThreadSafe() const override { return true; } protected: - Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override { - return default_allocator_->Allocate(size, attr).release(); + Allocation* AllocateImpl(size_t size) override { + return default_allocator_->Allocate(size).release(); } protected: @@ -185,19 +190,36 @@ class CUDAPinnedChunkedAllocator : public ChunkedAllocator { class AllocatorFacadePrivate { public: - std::map> allocators_; - - ~AllocatorFacadePrivate() = default; - AllocatorFacadePrivate() { - if (GetAllocatorStrategy() == AllocatorStrategy::kLegacy) { - InitLegacyAllocator(); - } else { - InitCPUAllocator(); - InitCUDAAllocator(); - InitCUDAPinnedAllocator(); - WrapZeroSizeAllocator(); + auto strategy = GetAllocatorStrategy(); + switch (strategy) { + case AllocatorStrategy::kLegacy: { + InitLegacyAllocator(); + break; + } + case AllocatorStrategy::kNaiveBestFit: { + InitCPUAllocator(); + InitCUDAAllocator(); + InitCUDAPinnedAllocator(); + break; + } + default: { + PADDLE_THROW("Unsupported allocator strategy: %d", + static_cast(strategy)); + } + } + InitZeroSizeAllocators(); + } + + inline const std::shared_ptr& GetAllocator( + const platform::Place& place, size_t size) { + const auto& allocators = (size > 0 ? allocators_ : zero_size_allocators_); + auto iter = allocators.find(place); + if (iter == allocators.end()) { + throw BadAlloc( + string::Sprintf("No such allocator for the place, %s", place)); } + return iter->second; } private: @@ -235,12 +257,40 @@ class AllocatorFacadePrivate { #endif } - void WrapZeroSizeAllocator() { - for (auto& pair : allocators_) { - pair.second = - std::make_shared(pair.second, pair.first); + class ZeroSizeAllocator : public Allocator { + public: + explicit ZeroSizeAllocator(platform::Place place) : place_(place) {} + + protected: + Allocation* AllocateImpl(size_t size) override { + return new Allocation(nullptr, 0, place_); + } + + void FreeImpl(Allocation* allocation) override { delete allocation; } + + private: + platform::Place place_; + }; + + void InitZeroSizeAllocators() { + std::vector places; + places.emplace_back(platform::CPUPlace()); +#ifdef PADDLE_WITH_CUDA + int device_count = platform::GetCUDADeviceCount(); + for (int dev_id = 0; dev_id < device_count; ++dev_id) { + places.emplace_back(platform::CUDAPlace(dev_id)); + } + places.emplace_back(platform::CUDAPinnedPlace()); +#endif + + for (auto& p : places) { + zero_size_allocators_[p] = std::make_shared(p); } } + + private: + std::map> allocators_; + std::map> zero_size_allocators_; }; // Pimpl. Make interface clean. @@ -253,19 +303,13 @@ AllocatorFacade& AllocatorFacade::Instance() { } std::shared_ptr AllocatorFacade::AllocShared( - const platform::Place& place, size_t size, Allocator::Attr attr) { - return std::shared_ptr(Alloc(place, size, attr).release(), - AllocationDeleter()); + const platform::Place& place, size_t size) { + return std::shared_ptr(Alloc(place, size)); } -AllocationPtr AllocatorFacade::Alloc(const platform::Place& place, size_t size, - Allocator::Attr attr) { - auto it = m_->allocators_.find(place); - if (it == m_->allocators_.end()) { - throw BadAlloc( - string::Sprintf("No such allocator for the place, %s", place)); - } - return m_->allocators_.at(place)->Allocate(size, attr); +AllocationPtr AllocatorFacade::Alloc(const platform::Place& place, + size_t size) { + return m_->GetAllocator(place, size)->Allocate(size); } } // namespace allocation diff --git a/paddle/fluid/memory/allocation/allocator_facade.h b/paddle/fluid/memory/allocation/allocator_facade.h index 16da30bec0d9f524bd076fe76d15c2fcfa7edd3a..64b6fe25c352e82d6320e26d95efb61f3cb4a5b1 100644 --- a/paddle/fluid/memory/allocation/allocator_facade.h +++ b/paddle/fluid/memory/allocation/allocator_facade.h @@ -38,13 +38,11 @@ class AllocatorFacade { static AllocatorFacade& Instance(); // Allocate a shared allocation. - std::shared_ptr AllocShared( - const platform::Place& place, size_t size, - Allocator::Attr attr = Allocator::kDefault); + std::shared_ptr AllocShared(const platform::Place& place, + size_t size); // Allocate a unique allocation. - AllocationPtr Alloc(const platform::Place& place, size_t size, - Allocator::Attr attr = Allocator::kDefault); + AllocationPtr Alloc(const platform::Place& place, size_t size); // TODO(yy): Allocate a Copy-On-Write allocation? private: diff --git a/paddle/fluid/memory/allocation/allocator_strategy.cc b/paddle/fluid/memory/allocation/allocator_strategy.cc index 8cebda9005b29b5b3259de0830c42eb10ef90e66..fff94c01e709613603eea7150a08df3c2611dec2 100644 --- a/paddle/fluid/memory/allocation/allocator_strategy.cc +++ b/paddle/fluid/memory/allocation/allocator_strategy.cc @@ -19,16 +19,22 @@ DEFINE_string( allocator_strategy, "legacy", "The allocation strategy. Legacy means the original allocator of Fluid." - "New means the experimental allocators of Fluid. in [legacy, new]"); + "naive_best_fit means the experimental best fit allocator. " + "allocator. Enum in [legacy, naive_best_fit]."); namespace paddle { namespace memory { namespace allocation { static AllocatorStrategy GetStrategyFromFlag() { - return FLAGS_allocator_strategy == "legacy" - ? AllocatorStrategy::kLegacy - : AllocatorStrategy::kNaiveBestFit; + if (FLAGS_allocator_strategy == "legacy") { + return AllocatorStrategy::kLegacy; + } else if (FLAGS_allocator_strategy == "naive_best_fit") { + return AllocatorStrategy::kNaiveBestFit; + } else { + PADDLE_THROW("Unsupported allocator strategy: %s", + FLAGS_allocator_strategy); + } } AllocatorStrategy GetAllocatorStrategy() { diff --git a/paddle/fluid/memory/allocation/auto_increment_allocator.cc b/paddle/fluid/memory/allocation/auto_increment_allocator.cc index c4785d2078601d7f9c5eeb7b902c7d1020340214..bafa82f18c7ee1f92ac4f0ad6634a06620f46c6a 100644 --- a/paddle/fluid/memory/allocation/auto_increment_allocator.cc +++ b/paddle/fluid/memory/allocation/auto_increment_allocator.cc @@ -34,14 +34,13 @@ std::shared_ptr AutoIncrementAllocator::CreateNewAllocator() { "bug."); return underlying_allocators_[old_size]; } -Allocation *AutoIncrementAllocator::AllocateImpl(size_t size, - Allocator::Attr attr) { +Allocation *AutoIncrementAllocator::AllocateImpl(size_t size) { auto cur = prev_success_allocator_.load(); size_t retry_count = allocator_num_.load(); size_t allocator_num = retry_count; while (retry_count-- > 0) { // until there retry count is zero try { - auto res = underlying_allocators_[cur]->Allocate(size, attr); + auto res = underlying_allocators_[cur]->Allocate(size); prev_success_allocator_ = cur; return res.release(); } catch (BadAlloc &) { @@ -61,7 +60,7 @@ Allocation *AutoIncrementAllocator::AllocateImpl(size_t size, // the newly created allocator by the first allocation request. for (cur = allocator_num; cur < allocator_num_; ++cur) { try { - auto ret = underlying_allocators_[cur]->Allocate(size, attr); + auto ret = underlying_allocators_[cur]->Allocate(size); prev_success_allocator_ = cur; return ret.release(); } catch (BadAlloc &) { @@ -70,7 +69,7 @@ Allocation *AutoIncrementAllocator::AllocateImpl(size_t size, } } // No suitable allocator - return CreateNewAllocator()->Allocate(size, attr).release(); + return CreateNewAllocator()->Allocate(size).release(); } } // namespace allocation diff --git a/paddle/fluid/memory/allocation/auto_increment_allocator.h b/paddle/fluid/memory/allocation/auto_increment_allocator.h index 382588f17a9748b1b0a356c0469c683f6c904778..068cda473d6e40bd0ac64a0b9e475336882d5edd 100644 --- a/paddle/fluid/memory/allocation/auto_increment_allocator.h +++ b/paddle/fluid/memory/allocation/auto_increment_allocator.h @@ -19,6 +19,7 @@ #include #include // NOLINT #include // NOLINT +#include #include #include "paddle/fluid/memory/allocation/allocator.h" @@ -60,7 +61,7 @@ class AutoIncrementAllocator : public Allocator { std::shared_ptr CreateNewAllocator(); protected: - Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override; + Allocation* AllocateImpl(size_t size) override; private: AllocatorCreator creator_; diff --git a/paddle/fluid/memory/allocation/best_fit_allocator.cc b/paddle/fluid/memory/allocation/best_fit_allocator.cc index e3d6c2f511ef083ef9ecc1fe8df96051b2b85cc2..72ee4e5411c21e172166e71fb8baa961ae2a63af 100644 --- a/paddle/fluid/memory/allocation/best_fit_allocator.cc +++ b/paddle/fluid/memory/allocation/best_fit_allocator.cc @@ -109,7 +109,7 @@ size_t BestFitAllocator::NumFreeChunks() const { } return num; } -void BestFitAllocator::Free(Allocation* allocation) { +void BestFitAllocator::FreeImpl(Allocation* allocation) { auto* bf_allocation = dynamic_cast(allocation); PADDLE_ENFORCE_NOT_NULL(bf_allocation, "The input allocation is not BestFitAllocation."); @@ -140,7 +140,7 @@ void BestFitAllocator::Free(Allocation* allocation) { InsertFreeNode(chunk_it); delete allocation; } -Allocation* BestFitAllocator::AllocateImpl(size_t size, Allocator::Attr attr) { +Allocation* BestFitAllocator::AllocateImpl(size_t size) { auto highest_set_bit = static_cast(HighestBitPos(size)); MapIt map_it; for (; highest_set_bit < free_chunks_.size(); ++highest_set_bit) { diff --git a/paddle/fluid/memory/allocation/best_fit_allocator.h b/paddle/fluid/memory/allocation/best_fit_allocator.h index 4f10f2b53e8543d4197097f1cae8de765bceeb0f..64a552e4fd2af1f661e3174e5041ffc71f74fa2c 100644 --- a/paddle/fluid/memory/allocation/best_fit_allocator.h +++ b/paddle/fluid/memory/allocation/best_fit_allocator.h @@ -119,8 +119,8 @@ class BestFitAllocator : public Allocator { void InsertFreeNode(const ListIt& it); protected: - void Free(Allocation* allocation) override; - Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override; + void FreeImpl(Allocation* allocation) override; + Allocation* AllocateImpl(size_t size) override; private: Allocation* allocation_; // not owned diff --git a/paddle/fluid/memory/allocation/best_fit_allocator_test.cc b/paddle/fluid/memory/allocation/best_fit_allocator_test.cc index b274b05562b15856276b1c88d3504fda1ecafacc..7e5207e6345bbd8ec02fdc897466c269779e2830 100644 --- a/paddle/fluid/memory/allocation/best_fit_allocator_test.cc +++ b/paddle/fluid/memory/allocation/best_fit_allocator_test.cc @@ -13,8 +13,10 @@ // limitations under the License. #include "paddle/fluid/memory/allocation/best_fit_allocator.h" +#include #include #include // NOLINT +#include #include #include "gtest/gtest.h" #include "paddle/fluid/memory/allocation/cpu_allocator.h" @@ -33,10 +35,10 @@ class StubAllocation : public Allocation { TEST(BestFitAllocator, test_allocation) { StubAllocation stub(4UL * 1024 * 1024 * 1024); BestFitAllocator allocator(&stub); - { auto allocation = allocator.Allocate(64, allocator.kDefault); } + { auto allocation = allocator.Allocate(64); } { - auto allocation = allocator.Allocate(80, allocator.kDefault); + auto allocation = allocator.Allocate(80); { auto best_fit_allocation = @@ -48,10 +50,10 @@ TEST(BestFitAllocator, test_allocation) { ASSERT_EQ(allocation->ptr(), nullptr); } - auto allocation2 = allocator.Allocate(60, allocator.kDefault); - auto allocation3 = allocator.Allocate(90, allocator.kDefault); + auto allocation2 = allocator.Allocate(60); + auto allocation3 = allocator.Allocate(90); allocation2.reset(); - allocation2 = allocator.Allocate(30, allocator.kDefault); + allocation2 = allocator.Allocate(30); { auto best_fit_allocation = @@ -59,7 +61,7 @@ TEST(BestFitAllocator, test_allocation) { ASSERT_EQ(best_fit_allocation->ChunkIterator()->offset_, 80); } allocation2.reset(); - allocation2 = allocator.Allocate(60, allocator.kDefault); + allocation2 = allocator.Allocate(60); { auto best_fit_allocation = @@ -70,7 +72,7 @@ TEST(BestFitAllocator, test_allocation) { allocation.reset(); allocation2.reset(); - allocation = allocator.Allocate(80 + 60, allocator.kDefault); + allocation = allocator.Allocate(80 + 60); { auto best_fit_allocation = dynamic_cast(allocation.get()); @@ -79,8 +81,8 @@ TEST(BestFitAllocator, test_allocation) { allocation.reset(); - allocation = allocator.Allocate(80, allocator.kDefault); - allocation2 = allocator.Allocate(60, allocator.kDefault); + allocation = allocator.Allocate(80); + allocation2 = allocator.Allocate(60); allocation = nullptr; allocation2 = nullptr; allocation3 = nullptr; @@ -91,8 +93,7 @@ TEST(BestFitAllocator, test_allocation) { TEST(BestFitAllocator, test_concurrent_cpu_allocation) { CPUAllocator allocator; - auto global_allocation = - allocator.Allocate(256UL * 1024 * 1024, allocator.kDefault); + auto global_allocation = allocator.Allocate(256UL * 1024 * 1024); std::unique_ptr best_fit_allocator( new BestFitAllocator(global_allocation.get())); @@ -106,8 +107,8 @@ TEST(BestFitAllocator, test_concurrent_cpu_allocation) { for (size_t i = 0; i < 128; ++i) { size_t allocate_size = dist(engine); - auto allocation = locked_allocator.Allocate( - sizeof(size_t) * allocate_size, locked_allocator.kDefault); + auto allocation = + locked_allocator.Allocate(sizeof(size_t) * allocate_size); size_t* data = reinterpret_cast(allocation->ptr()); diff --git a/paddle/fluid/memory/allocation/best_fit_allocator_test.cu b/paddle/fluid/memory/allocation/best_fit_allocator_test.cu index fdd5b43ad4aa8024efee314ca949445fefbef067..eb24ba84c886e3393cf36b6f764d7b33e76defeb 100644 --- a/paddle/fluid/memory/allocation/best_fit_allocator_test.cu +++ b/paddle/fluid/memory/allocation/best_fit_allocator_test.cu @@ -12,6 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include #include #include // NOLINT #include @@ -36,8 +37,7 @@ struct ForEachFill { TEST(BestFitAllocator, concurrent_cuda) { CUDAAllocator allocator(platform::CUDAPlace(0)); // 256 MB - auto cuda_allocation = - allocator.Allocate(256U * 1024 * 1024, allocator.kDefault); + auto cuda_allocation = allocator.Allocate(256U * 1024 * 1024); LockedAllocator concurrent_allocator( std::unique_ptr(new BestFitAllocator(cuda_allocation.get()))); @@ -50,8 +50,8 @@ TEST(BestFitAllocator, concurrent_cuda) { for (size_t i = 0; i < 128; ++i) { size_t allocate_size = dist(engine); - auto allocation = concurrent_allocator.Allocate( - sizeof(size_t) * allocate_size, concurrent_allocator.kDefault); + auto allocation = + concurrent_allocator.Allocate(sizeof(size_t) * allocate_size); size_t* data = reinterpret_cast(allocation->ptr()); diff --git a/paddle/fluid/memory/allocation/buffered_allocator.cc b/paddle/fluid/memory/allocation/buffered_allocator.cc index fc75abc9dfee6c9df5bc87faa493002cc1fe6298..d80616b7a8d39a5e1074ce240d9c4ddb069b212a 100644 --- a/paddle/fluid/memory/allocation/buffered_allocator.cc +++ b/paddle/fluid/memory/allocation/buffered_allocator.cc @@ -16,17 +16,16 @@ #include #include #include -#include "paddle/fluid/memory/allocation/allocation_with_underlying.h" namespace paddle { namespace memory { namespace allocation { -BufferedAllocator::BufferedAllocator(std::unique_ptr &&allocator) +BufferedAllocator::BufferedAllocator(std::shared_ptr allocator) : underlying_allocator_(std::move(allocator)) { PADDLE_ENFORCE_NOT_NULL( underlying_allocator_, - "Underlying allocator of BufferedAllocator must be unmanaged"); + "Underlying allocator of BufferedAllocator must not be null"); if (underlying_allocator_->IsAllocThreadSafe()) { mtx_.reset(new std::mutex()); } @@ -41,37 +40,35 @@ void BufferedAllocator::FreeCache(size_t size) { while (!allocations_.empty()) { // free the largest auto it = --allocations_.end(); cur += it->second->size(); - delete it->second.release(); + underlying_allocator_->Free(it->second.release()); allocations_.erase(it); if (cur >= size) return; } } -bool BufferedAllocator::IsAllocThreadSafe() const { - return this->underlying_allocator_->IsAllocThreadSafe(); -} -void BufferedAllocator::Free(Allocation *allocation) { +bool BufferedAllocator::IsAllocThreadSafe() const { return mtx_ != nullptr; } + +void BufferedAllocator::FreeImpl(Allocation *allocation) { platform::LockGuardPtr guard(mtx_); allocations_.emplace(allocation->size(), AllocationPtr(allocation)); } -Allocation *BufferedAllocator::AllocateImpl(size_t size, Allocator::Attr attr) { + +Allocation *BufferedAllocator::AllocateImpl(size_t size) { { platform::LockGuardPtr guard(mtx_); auto it = allocations_.lower_bound(size); if (it != allocations_.end() && it->first < size * 2) { AllocationPtr result(std::move(it->second)); allocations_.erase(it); - return new AllocationWithUnderlying(std::move(result)); + return result.release(); } } try { - return new AllocationWithUnderlying( - underlying_allocator_->Allocate(size, attr)); + return underlying_allocator_->Allocate(size).release(); } catch (BadAlloc &) { FreeCache(size); - return new AllocationWithUnderlying( - underlying_allocator_->Allocate(size, attr)); + return underlying_allocator_->Allocate(size).release(); } } diff --git a/paddle/fluid/memory/allocation/buffered_allocator.h b/paddle/fluid/memory/allocation/buffered_allocator.h index d44a3f85beba712b1e735ba14008689bce7d0d64..fd0996f7748ef407262dba7bca705af9b5fb9674 100644 --- a/paddle/fluid/memory/allocation/buffered_allocator.h +++ b/paddle/fluid/memory/allocation/buffered_allocator.h @@ -31,7 +31,7 @@ namespace allocation { // underlying_allocator_ class BufferedAllocator : public Allocator { public: - explicit BufferedAllocator(std::unique_ptr &&allocator); + explicit BufferedAllocator(std::shared_ptr allocator); ~BufferedAllocator(); @@ -44,11 +44,11 @@ class BufferedAllocator : public Allocator { void FreeCache(size_t size); protected: - void Free(Allocation *allocation) override; - Allocation *AllocateImpl(size_t size, Allocator::Attr attr) override; + void FreeImpl(Allocation *allocation) override; + Allocation *AllocateImpl(size_t size) override; private: - std::unique_ptr underlying_allocator_; + std::shared_ptr underlying_allocator_; std::multimap allocations_; std::unique_ptr mtx_; }; diff --git a/paddle/fluid/memory/allocation/buffered_allocator_test.cc b/paddle/fluid/memory/allocation/buffered_allocator_test.cc index c8bd5292ca0f6c3e7ebdc7f5908523b0b7c8ba3a..e4825233d58c7386bc1b7456cdc5c11f03f6b90e 100644 --- a/paddle/fluid/memory/allocation/buffered_allocator_test.cc +++ b/paddle/fluid/memory/allocation/buffered_allocator_test.cc @@ -14,7 +14,6 @@ #include "paddle/fluid/memory/allocation/buffered_allocator.h" #include -#include #include #include "paddle/fluid/memory/allocation/best_fit_allocator.h" #include "paddle/fluid/memory/allocation/cpu_allocator.h" @@ -37,7 +36,7 @@ inline std::unique_ptr GetBufferedAllocator( TEST(buffered_allocator, thread_safety) { std::unique_ptr allocator(new CPUAllocator()); - auto chunk = allocator->Allocate(1 << 20, allocator->kDefault); + auto chunk = allocator->Allocate(1 << 20); { auto buf_allocator = GetBufferedAllocator(chunk.get(), true); ASSERT_EQ(buf_allocator->IsAllocThreadSafe(), true); @@ -66,14 +65,14 @@ class StubAllocator : public Allocator { size_t GetFreeCount() const { return destruct_count_; } protected: - void Free(Allocation *allocation) override { + void FreeImpl(Allocation *allocation) override { auto *alloc = dynamic_cast(allocation); PADDLE_ENFORCE_NOT_NULL(alloc); if (alloc->ptr()) delete[] static_cast(alloc->ptr()); ++destruct_count_; delete allocation; } - Allocation *AllocateImpl(size_t size, Allocator::Attr attr) override { + Allocation *AllocateImpl(size_t size) override { ++construct_count_; if (size == 0) { return new StubAllocation(nullptr, 0, platform::CPUPlace()); @@ -99,7 +98,7 @@ TEST(buffered_allocator, lazy_free) { { underlying_allocator->ResetCounter(); - auto x = allocator->Allocate(1025, allocator->kDefault); + auto x = allocator->Allocate(1025); ASSERT_EQ(underlying_allocator->GetAllocCount(), kOne); ASSERT_EQ(underlying_allocator->GetFreeCount(), kZero); x = nullptr; @@ -108,10 +107,10 @@ TEST(buffered_allocator, lazy_free) { { underlying_allocator->ResetCounter(); - auto x = allocator->Allocate(900, allocator->kDefault); + auto x = allocator->Allocate(900); ASSERT_EQ(underlying_allocator->GetAllocCount(), kZero); ASSERT_EQ(underlying_allocator->GetFreeCount(), kZero); - auto y = allocator->Allocate(2048, allocator->kDefault); + auto y = allocator->Allocate(2048); ASSERT_EQ(underlying_allocator->GetAllocCount(), kOne); ASSERT_EQ(underlying_allocator->GetFreeCount(), kZero); x = nullptr; @@ -130,13 +129,13 @@ TEST(buffered_allocator, lazy_free) { TEST(buffered_allocator, garbage_collection) { std::unique_ptr cpu_allocator(new CPUAllocator()); - auto chunk = cpu_allocator->Allocate(2048, cpu_allocator->kDefault); + auto chunk = cpu_allocator->Allocate(2048); auto allocator = GetBufferedAllocator(chunk.get(), false); - auto x1 = allocator->Allocate(1600, allocator->kDefault); - auto x2 = allocator->Allocate(400, allocator->kDefault); + auto x1 = allocator->Allocate(1600); + auto x2 = allocator->Allocate(400); x1 = nullptr; x2 = nullptr; - auto x3 = allocator->Allocate(1600, allocator->kDefault); + auto x3 = allocator->Allocate(1600); ASSERT_NE(x3, nullptr); ASSERT_NE(x3->ptr(), nullptr); } diff --git a/paddle/fluid/memory/allocation/conditional_allocator.cc b/paddle/fluid/memory/allocation/conditional_allocator.cc index 96a818e03e507c6de720344288312dc2af2ae647..373afb1bd6e1ff1582f8aa737ac1ff19309909de 100644 --- a/paddle/fluid/memory/allocation/conditional_allocator.cc +++ b/paddle/fluid/memory/allocation/conditional_allocator.cc @@ -13,14 +13,14 @@ // limitations under the License. #include "paddle/fluid/memory/allocation/conditional_allocator.h" +#include namespace paddle { namespace memory { namespace allocation { ConditionalAllocator& ConditionalAllocator::AddAllocator( - std::function func, - std::shared_ptr allocator) { + std::function func, std::shared_ptr allocator) { underlying_allocators_.emplace_back(std::move(func), std::move(allocator)); return *this; } @@ -33,11 +33,10 @@ bool ConditionalAllocator::IsAllocThreadSafe() const { }); } -Allocation* ConditionalAllocator::AllocateImpl(size_t size, - Allocator::Attr attr) { +Allocation* ConditionalAllocator::AllocateImpl(size_t size) { for (auto& pair : underlying_allocators_) { - if (pair.first(size, attr)) { - return pair.second->Allocate(size, attr).release(); + if (pair.first(size)) { + return pair.second->Allocate(size).release(); } } throw BadAlloc("No suitable allocator"); diff --git a/paddle/fluid/memory/allocation/conditional_allocator.h b/paddle/fluid/memory/allocation/conditional_allocator.h index 94cba4432ed4f72c0a75da9b31d48611a8404ad3..61c3670803a3c5a87a5bbf640ec584b611d06140 100644 --- a/paddle/fluid/memory/allocation/conditional_allocator.h +++ b/paddle/fluid/memory/allocation/conditional_allocator.h @@ -14,6 +14,7 @@ #pragma once #include +#include #include #include #include "paddle/fluid/memory/allocation/allocator.h" @@ -28,13 +29,10 @@ namespace allocation { // For example: // // auto* cond_allocator = new ConditionalAllocator(); -// cond_allocator->AddAllocator([](size_t size, Attr attr){ +// cond_allocator->AddAllocator([](size_t size){ // // if size > 10 // return size > 10; -// }, allocator_a).AddAllocator([](size_t size, Attr attr){ -// // elif attr is kDefault -// return attr == kDefault; -// }, allocator_b).AddAllocator([](size_t size, Attr attr){ +// }, allocator_b).AddAllocator([](size_t size){ // // else // return true; // }, allocator_c); @@ -42,17 +40,17 @@ class ConditionalAllocator : public Allocator { public: ConditionalAllocator() = default; - ConditionalAllocator& AddAllocator(std::function func, + ConditionalAllocator& AddAllocator(std::function func, std::shared_ptr allocator); bool IsAllocThreadSafe() const override; protected: - Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override; + Allocation* AllocateImpl(size_t size) override; private: using AllocatorWithCond = - std::pair, std::shared_ptr>; + std::pair, std::shared_ptr>; std::vector underlying_allocators_; }; diff --git a/paddle/fluid/memory/allocation/cpu_allocator.cc b/paddle/fluid/memory/allocation/cpu_allocator.cc index cc81a6f7b8b1950b07b6fb1571b53d9b5ddb1b9f..580cf1af56ab0ad2f096f9b6fefaff0ba0e501a0 100644 --- a/paddle/fluid/memory/allocation/cpu_allocator.cc +++ b/paddle/fluid/memory/allocation/cpu_allocator.cc @@ -20,25 +20,27 @@ namespace paddle { namespace memory { namespace allocation { -CPUAllocation::CPUAllocation(void *ptr, size_t size) - : Allocation(ptr, size, platform::CPUPlace()) {} - bool CPUAllocator::IsAllocThreadSafe() const { return true; } -void CPUAllocator::Free(Allocation *allocation) { - PADDLE_ENFORCE_NOT_NULL(dynamic_cast(allocation)); - free(allocation->ptr()); +void CPUAllocator::FreeImpl(Allocation *allocation) { + void *p = allocation->ptr(); +#ifdef _WIN32 + _aligned_free(p); +#else + free(p); +#endif delete allocation; } -Allocation *CPUAllocator::AllocateImpl(size_t size, Allocator::Attr attr) { - void *ptr; - auto status = posix_memalign(&ptr, kAlignment, size); - if (UNLIKELY(status) != 0) { - throw BadAlloc(string::Sprintf("Cannot allocate cpu memory %d. Errno is %d", - size, status)); - } - return new CPUAllocation(ptr, size); +Allocation *CPUAllocator::AllocateImpl(size_t size) { + void *p; +#ifdef _WIN32 + p = _aligned_malloc(size, kAlignment); +#else + PADDLE_ENFORCE_EQ(posix_memalign(&p, kAlignment, size), 0, "Alloc %ld error!", + size); +#endif + return new Allocation(p, size, platform::CPUPlace()); } } // namespace allocation } // namespace memory diff --git a/paddle/fluid/memory/allocation/cpu_allocator.h b/paddle/fluid/memory/allocation/cpu_allocator.h index 26d3643f4edff1f2d71b1c761e915a6dacb485ad..058ff63381658da698841c839425dec000a748da 100644 --- a/paddle/fluid/memory/allocation/cpu_allocator.h +++ b/paddle/fluid/memory/allocation/cpu_allocator.h @@ -31,20 +31,14 @@ namespace allocation { // // NOTE(yy): It is no need to use `BestFitAllocator` in CPU. We can import // an open-sourced allocator into Paddle. -class CPUAllocator; -class CPUAllocation : public Allocation { - public: - CPUAllocation(void* ptr, size_t size); -}; - class CPUAllocator : public Allocator { public: - constexpr static size_t kAlignment = 64u; + constexpr static size_t kAlignment = 4096UL; bool IsAllocThreadSafe() const override; protected: - void Free(Allocation* allocation) override; - Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override; + void FreeImpl(Allocation* allocation) override; + Allocation* AllocateImpl(size_t size) override; }; } // namespace allocation } // namespace memory diff --git a/paddle/fluid/memory/allocation/cuda_allocator.cc b/paddle/fluid/memory/allocation/cuda_allocator.cc index 430bf0be98e08787ac4412a8b6e0fcc310ffe2b4..349c71cece16898da33d1dac3e979c4694b6f7b7 100644 --- a/paddle/fluid/memory/allocation/cuda_allocator.cc +++ b/paddle/fluid/memory/allocation/cuda_allocator.cc @@ -23,16 +23,15 @@ namespace paddle { namespace memory { namespace allocation { bool CUDAAllocator::IsAllocThreadSafe() const { return true; } -void CUDAAllocator::Free(Allocation* allocation) { +void CUDAAllocator::FreeImpl(Allocation* allocation) { platform::CUDADeviceGuard guard(place_.device); - auto* cuda_allocation = dynamic_cast(allocation); - PADDLE_ENFORCE_NOT_NULL(cuda_allocation); - PADDLE_ENFORCE_EQ(boost::get(cuda_allocation->place()), + PADDLE_ENFORCE_EQ(boost::get(allocation->place()), place_); PADDLE_ENFORCE(cudaFree(allocation->ptr())); delete allocation; } -Allocation* CUDAAllocator::AllocateImpl(size_t size, Allocator::Attr attr) { + +Allocation* CUDAAllocator::AllocateImpl(size_t size) { platform::CUDADeviceGuard guard(place_.device); void* ptr; auto status = cudaMalloc(&ptr, size); @@ -41,8 +40,9 @@ Allocation* CUDAAllocator::AllocateImpl(size_t size, Allocator::Attr attr) { "Cannot allocate %d on GPU %d, cuda status %d, %s", size, place_.device, status, cudaGetErrorString(status))); } - return new CUDAAllocation(ptr, size, platform::Place(place_)); + return new Allocation(ptr, size, platform::Place(place_)); } + } // namespace allocation } // namespace memory } // namespace paddle diff --git a/paddle/fluid/memory/allocation/cuda_allocator.h b/paddle/fluid/memory/allocation/cuda_allocator.h index 63726f5820b1c81565117c7a9bf798c17c9681f6..886f6e7a327f70068c6fabb6328f927bf71b2881 100644 --- a/paddle/fluid/memory/allocation/cuda_allocator.h +++ b/paddle/fluid/memory/allocation/cuda_allocator.h @@ -20,13 +20,6 @@ namespace paddle { namespace memory { namespace allocation { -// CUDA System allocator and allocation. -// Just a flag type. -class CUDAAllocation : public Allocation { - public: - using Allocation::Allocation; -}; - class CUDAAllocator : public Allocator { public: explicit CUDAAllocator(const platform::CUDAPlace& place) : place_(place) {} @@ -35,8 +28,8 @@ class CUDAAllocator : public Allocator { bool IsAllocThreadSafe() const override; protected: - void Free(Allocation* allocation) override; - Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override; + void FreeImpl(Allocation* allocation) override; + Allocation* AllocateImpl(size_t size) override; private: platform::CUDAPlace place_; diff --git a/paddle/fluid/memory/allocation/legacy_allocator.cc b/paddle/fluid/memory/allocation/legacy_allocator.cc index 2ecb44ff15fec23e9b2a0045a959a2f6ed8a0a8c..4adc0aabf4fb7cfc782f17801d51bd70404c21de 100644 --- a/paddle/fluid/memory/allocation/legacy_allocator.cc +++ b/paddle/fluid/memory/allocation/legacy_allocator.cc @@ -200,12 +200,12 @@ void *Alloc(const platform::CUDAPlace &place, platform::GpuMemoryUsage(&avail, &total); LOG(FATAL) << "Cannot allocate " << string::HumanReadableSize(size) << " in GPU " << place.device << ", available " - << string::HumanReadableSize(avail) << "total " << total - << "GpuMinChunkSize " + << string::HumanReadableSize(avail) << ", total " + << string::HumanReadableSize(total) << ", GpuMinChunkSize " << string::HumanReadableSize(buddy_allocator->GetMinChunkSize()) - << "GpuMaxChunkSize " + << ", GpuMaxChunkSize " << string::HumanReadableSize(buddy_allocator->GetMaxChunkSize()) - << "GPU memory used: " + << ", GPU memory used: " << string::HumanReadableSize(Used(place)); } else { if (FLAGS_benchmark) { @@ -339,7 +339,7 @@ size_t Usage::operator()(const platform::CUDAPinnedPlace &cuda_pinned) const { namespace allocation { LegacyMemMonitor GPUMemMonitor; -Allocation *LegacyAllocator::AllocateImpl(size_t size, Allocator::Attr attr) { +Allocation *LegacyAllocator::AllocateImpl(size_t size) { void *ptr = boost::apply_visitor(legacy::AllocVisitor(size), place_); auto *tmp_alloc = new Allocation(ptr, size, place_); platform::MemEvenRecorder::Instance().PushMemRecord( @@ -347,7 +347,7 @@ Allocation *LegacyAllocator::AllocateImpl(size_t size, Allocator::Attr attr) { return tmp_alloc; } -void LegacyAllocator::Free(Allocation *allocation) { +void LegacyAllocator::FreeImpl(Allocation *allocation) { boost::apply_visitor( legacy::FreeVisitor(allocation->ptr(), allocation->size()), allocation->place()); diff --git a/paddle/fluid/memory/allocation/legacy_allocator.h b/paddle/fluid/memory/allocation/legacy_allocator.h index d9bdae153da6439598f76f5cac226897e6e0c596..c7efb5fd2e5a9b4292f83e6ecba1549fb293c56c 100644 --- a/paddle/fluid/memory/allocation/legacy_allocator.h +++ b/paddle/fluid/memory/allocation/legacy_allocator.h @@ -72,8 +72,8 @@ class LegacyAllocator : public Allocator { explicit LegacyAllocator(const platform::Place &p) : place_(p) {} protected: - Allocation *AllocateImpl(size_t size, Allocator::Attr attr) override; - void Free(Allocation *allocation) override; + Allocation *AllocateImpl(size_t size) override; + void FreeImpl(Allocation *allocation) override; private: platform::Place place_; diff --git a/paddle/fluid/memory/allocation/locked_allocator.cc b/paddle/fluid/memory/allocation/locked_allocator.cc index 62d768c580607f32db8c49eb3d62f0f32c9dbeeb..a912807645bafee3c1cb63f03ff456418033b416 100644 --- a/paddle/fluid/memory/allocation/locked_allocator.cc +++ b/paddle/fluid/memory/allocation/locked_allocator.cc @@ -15,8 +15,8 @@ #include "paddle/fluid/memory/allocation/locked_allocator.h" #include // NOLINT #include -#include "paddle/fluid/memory/allocation/allocation_with_underlying.h" #include "paddle/fluid/platform/lock_guard_ptr.h" + namespace paddle { namespace memory { namespace allocation { @@ -24,26 +24,24 @@ namespace allocation { bool LockedAllocator::IsAllocThreadSafe() const { return true; } LockedAllocator::LockedAllocator( - std::unique_ptr &&underlying_allocator) + std::shared_ptr underlying_allocator) : underlying_allocator_(std::move(underlying_allocator)) { PADDLE_ENFORCE_NOT_NULL(underlying_allocator_); if (!underlying_allocator_->IsAllocThreadSafe()) { mtx_.reset(new std::mutex()); } } -void LockedAllocator::Free(Allocation *allocation) { - { - platform::LockGuardPtr guard(mtx_); - reinterpret_cast(allocation) - ->allocation_.reset(); // Destroy inner allocation - } - delete allocation; + +void LockedAllocator::FreeImpl(Allocation *allocation) { + platform::LockGuardPtr guard(mtx_); + underlying_allocator_->Free(allocation); } -Allocation *LockedAllocator::AllocateImpl(size_t size, Allocator::Attr attr) { + +Allocation *LockedAllocator::AllocateImpl(size_t size) { platform::LockGuardPtr guard(mtx_); - return new AllocationWithUnderlying( - underlying_allocator_->Allocate(size, attr)); + return underlying_allocator_->Allocate(size).release(); } + } // namespace allocation } // namespace memory } // namespace paddle diff --git a/paddle/fluid/memory/allocation/locked_allocator.h b/paddle/fluid/memory/allocation/locked_allocator.h index 4967b9bb8d3ad101cff4657b0a45b49b76e2deb2..4af77e6e057f54d15dcb0248ba6cf36f6f00c2f1 100644 --- a/paddle/fluid/memory/allocation/locked_allocator.h +++ b/paddle/fluid/memory/allocation/locked_allocator.h @@ -24,15 +24,15 @@ namespace allocation { // A allocator to make underlying allocator thread safe. class LockedAllocator : public Allocator { public: - explicit LockedAllocator(std::unique_ptr &&underlying_allocator); + explicit LockedAllocator(std::shared_ptr underlying_allocator); bool IsAllocThreadSafe() const override; protected: - void Free(Allocation *allocation) override; - Allocation *AllocateImpl(size_t size, Allocator::Attr attr) override; + void FreeImpl(Allocation *allocation) override; + Allocation *AllocateImpl(size_t size) override; private: - std::unique_ptr underlying_allocator_; + std::shared_ptr underlying_allocator_; std::unique_ptr mtx_; }; diff --git a/paddle/fluid/memory/allocation/pinned_allocator.cc b/paddle/fluid/memory/allocation/pinned_allocator.cc index de81d12cca6ca280289371abdec225c9e2b8f4d0..35391167fe66b9b941e3a5359db452ced7995762 100644 --- a/paddle/fluid/memory/allocation/pinned_allocator.cc +++ b/paddle/fluid/memory/allocation/pinned_allocator.cc @@ -20,20 +20,14 @@ namespace paddle { namespace memory { namespace allocation { bool CPUPinnedAllocator::IsAllocThreadSafe() const { return true; } -void CPUPinnedAllocator::Free(Allocation *allocation) { - PADDLE_ENFORCE_NOT_NULL(dynamic_cast(allocation)); +void CPUPinnedAllocator::FreeImpl(Allocation *allocation) { PADDLE_ENFORCE(cudaFreeHost(allocation->ptr())); delete allocation; } -Allocation *CPUPinnedAllocator::AllocateImpl(size_t size, - Allocator::Attr attr) { - // PADDLE_ENFORCE_EQ( - // attr, kCrossDevice, - // "CPUPinnedAllocator should be used for Cross-Device Communication"); - +Allocation *CPUPinnedAllocator::AllocateImpl(size_t size) { void *ptr; PADDLE_ENFORCE(cudaHostAlloc(&ptr, size, cudaHostAllocPortable)); - return new CPUPinnedAllocation(ptr, size); + return new Allocation(ptr, size, platform::CUDAPinnedPlace()); } } // namespace allocation } // namespace memory diff --git a/paddle/fluid/memory/allocation/pinned_allocator.h b/paddle/fluid/memory/allocation/pinned_allocator.h index 42d0938f2afbb1efca8bfdd7035bc0eada30f06b..4f535ef33734a3c6f7048ae6538e4332e0c9e8e4 100644 --- a/paddle/fluid/memory/allocation/pinned_allocator.h +++ b/paddle/fluid/memory/allocation/pinned_allocator.h @@ -20,19 +20,13 @@ namespace memory { namespace allocation { // Allocator uses `cudaHostAlloc` -class CPUPinnedAllocation : public Allocation { - public: - CPUPinnedAllocation(void *ptr, size_t size) - : Allocation(ptr, size, platform::CUDAPinnedPlace()) {} -}; - class CPUPinnedAllocator : public Allocator { public: bool IsAllocThreadSafe() const override; protected: - void Free(Allocation *allocation) override; - Allocation *AllocateImpl(size_t size, Allocator::Attr attr) override; + void FreeImpl(Allocation *allocation) override; + Allocation *AllocateImpl(size_t size) override; }; } // namespace allocation diff --git a/paddle/fluid/memory/allocation/retry_allocator.cc b/paddle/fluid/memory/allocation/retry_allocator.cc index 981705051b449e6a35c2dcce9138dc2efae52920..bf14ed5db10fc475a7bbaa8bb6759f90c5a207de 100644 --- a/paddle/fluid/memory/allocation/retry_allocator.cc +++ b/paddle/fluid/memory/allocation/retry_allocator.cc @@ -13,30 +13,19 @@ // limitations under the License. #include "paddle/fluid/memory/allocation/retry_allocator.h" -#include "paddle/fluid/memory/allocation/allocation_with_underlying.h" namespace paddle { namespace memory { namespace allocation { -bool RetryAllocator::IsAllocThreadSafe() const { - return underlying_allocator_->IsAllocThreadSafe(); -} - -void RetryAllocator::Free(Allocation* allocation) { +void RetryAllocator::FreeImpl(Allocation* allocation) { // Delete underlying allocation first. - reinterpret_cast(allocation)->allocation_.reset(); - { - // notify all waited allocators, they can try to allocate memory after free. - std::lock_guard lock(mutex_); - cv_.notify_all(); - } - delete allocation; + underlying_allocator_->Free(allocation); + cv_.notify_all(); } -Allocation* RetryAllocator::AllocateImpl(size_t size, Allocator::Attr attr) { +Allocation* RetryAllocator::AllocateImpl(size_t size) { auto alloc_func = [&, this]() { - return new AllocationWithUnderlying( - underlying_allocator_->Allocate(size, attr)); + return underlying_allocator_->Allocate(size).release(); }; // In fact, we can unify the code of allocation success and failure // But it would add lock even when allocation success at the first time diff --git a/paddle/fluid/memory/allocation/retry_allocator.h b/paddle/fluid/memory/allocation/retry_allocator.h index 6ab8ca8fbec0077b2c95cf727731ca0095716197..7840a834472c831f500622535f270fcf39732a67 100644 --- a/paddle/fluid/memory/allocation/retry_allocator.h +++ b/paddle/fluid/memory/allocation/retry_allocator.h @@ -25,32 +25,25 @@ namespace paddle { namespace memory { namespace allocation { -class RetryAllocator; - class RetryAllocator : public Allocator { public: - RetryAllocator(std::unique_ptr&& allocator, size_t retry_ms) + RetryAllocator(std::shared_ptr allocator, size_t retry_ms) : underlying_allocator_(std::move(allocator)), retry_time_(retry_ms) { - EnforceCheck(); - } - - bool IsAllocThreadSafe() const override; - - private: - void EnforceCheck() { PADDLE_ENFORCE_NOT_NULL( - underlying_allocator_.get(), - "UnderlyingAllocator of RetryAllocator must be UnmanagedAllocator"); + underlying_allocator_, + "UnderlyingAllocator of RetryAllocator must not be null"); PADDLE_ENFORCE(underlying_allocator_->IsAllocThreadSafe(), "UnderlyingAllocator of RetryAllocator must be thread-safe"); } + bool IsAllocThreadSafe() const override { return true; } + protected: - void Free(Allocation* allocation) override; - Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override; + void FreeImpl(Allocation* allocation) override; + Allocation* AllocateImpl(size_t size) override; private: - std::unique_ptr underlying_allocator_; + std::shared_ptr underlying_allocator_; std::chrono::milliseconds retry_time_; std::mutex mutex_; std::condition_variable cv_; @@ -58,8 +51,6 @@ class RetryAllocator : public Allocator { // For debug, We can add an atomic integer to record how many memory sizes are // waited to allocate // std::atomic waited_allocate_size_{0}; - - friend class RetryAllocation; }; } // namespace allocation diff --git a/paddle/fluid/memory/allocation/retry_allocator_test.cc b/paddle/fluid/memory/allocation/retry_allocator_test.cc index 345b5f44d3de9b68017410156740886e08a81b15..4ac08d442d4bd3cb7edc4db020e5c3242b13b535 100644 --- a/paddle/fluid/memory/allocation/retry_allocator_test.cc +++ b/paddle/fluid/memory/allocation/retry_allocator_test.cc @@ -32,7 +32,7 @@ TEST(RetryAllocator, RetryAllocator) { CPUAllocator cpu_allocator; size_t size = (1 << 20); - auto cpu_allocation = cpu_allocator.Allocate(size, cpu_allocator.kDefault); + auto cpu_allocation = cpu_allocator.Allocate(size); std::unique_ptr best_fit_allocator( new BestFitAllocator(cpu_allocation.get())); diff --git a/paddle/fluid/memory/malloc.cc b/paddle/fluid/memory/malloc.cc index e414ad657a9447142d6e3a42fc7efc86f01e9c9f..5884433aaff115c053b10848b32f8610fcb69747 100644 --- a/paddle/fluid/memory/malloc.cc +++ b/paddle/fluid/memory/malloc.cc @@ -21,13 +21,12 @@ limitations under the License. */ namespace paddle { namespace memory { std::shared_ptr AllocShared(const platform::Place& place, - size_t size, Allocator::Attr attr) { - return allocation::AllocatorFacade::Instance().AllocShared(place, size, attr); + size_t size) { + return allocation::AllocatorFacade::Instance().AllocShared(place, size); } -AllocationPtr Alloc(const platform::Place& place, size_t size, - Allocator::Attr attr) { - return allocation::AllocatorFacade::Instance().Alloc(place, size, attr); +AllocationPtr Alloc(const platform::Place& place, size_t size) { + return allocation::AllocatorFacade::Instance().Alloc(place, size); } } // namespace memory diff --git a/paddle/fluid/memory/malloc.h b/paddle/fluid/memory/malloc.h index 916538b2a659d7d9503fdc337a4ba84fa21f77f9..6731203fccb67fc5ded018bbe2ca51878da1a4c3 100644 --- a/paddle/fluid/memory/malloc.h +++ b/paddle/fluid/memory/malloc.h @@ -23,12 +23,10 @@ using allocation::Allocation; using allocation::Allocator; using allocation::AllocationPtr; -extern std::shared_ptr AllocShared( - const platform::Place& place, size_t size, - Allocator::Attr attr = Allocator::kDefault); +extern std::shared_ptr AllocShared(const platform::Place& place, + size_t size); -extern AllocationPtr Alloc(const platform::Place& place, size_t size, - Allocator::Attr attr = Allocator::kDefault); +extern AllocationPtr Alloc(const platform::Place& place, size_t size); } // namespace memory } // namespace paddle diff --git a/paddle/fluid/memory/memcpy.cc b/paddle/fluid/memory/memcpy.cc index 1408163e4b5278ddcd65eb4f2900109d772a589a..c08d86eb213310b4e8dbac541c254867bb44b903 100644 --- a/paddle/fluid/memory/memcpy.cc +++ b/paddle/fluid/memory/memcpy.cc @@ -15,6 +15,7 @@ limitations under the License. */ #include "paddle/fluid/memory/memcpy.h" #include // for memcpy +#include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/profiler.h" namespace paddle { @@ -24,6 +25,7 @@ template <> void Copy(platform::CPUPlace, void* dst, platform::CPUPlace, const void* src, size_t num) { + if (UNLIKELY(num == 0)) return; std::memcpy(dst, src, num); } @@ -40,6 +42,7 @@ template <> void Copy( platform::CPUPlace dst_place, void* dst, platform::CUDAPlace src_place, const void* src, size_t num, cudaStream_t stream) { + if (UNLIKELY(num == 0)) return; platform::SetDeviceId(src_place.device); if (stream) { @@ -59,6 +62,8 @@ template <> void Copy( platform::CUDAPlace dst_place, void* dst, platform::CPUPlace src_place, const void* src, size_t num, cudaStream_t stream) { + if (UNLIKELY(num == 0)) return; + platform::SetDeviceId(dst_place.device); if (stream) { platform::RecordEvent record_event("GpuMemcpyAsync:CPU->GPU"); @@ -77,6 +82,8 @@ template <> void Copy( platform::CUDAPlace dst_place, void* dst, platform::CUDAPlace src_place, const void* src, size_t num, cudaStream_t stream) { + if (UNLIKELY(num == 0)) return; + if (dst_place == src_place) { platform::SetDeviceId(src_place.device); if (stream) { @@ -103,6 +110,7 @@ template <> void Copy( platform::CPUPlace dst_place, void* dst, platform::CUDAPinnedPlace src_place, const void* src, size_t num) { + if (UNLIKELY(num == 0)) return; std::memcpy(dst, src, num); } @@ -110,6 +118,7 @@ template <> void Copy( platform::CUDAPinnedPlace dst_place, void* dst, platform::CPUPlace src_place, const void* src, size_t num) { + if (UNLIKELY(num == 0)) return; std::memcpy(dst, src, num); } @@ -117,6 +126,7 @@ template <> void Copy( platform::CUDAPinnedPlace dst_place, void* dst, platform::CUDAPinnedPlace src_place, const void* src, size_t num) { + if (UNLIKELY(num == 0)) return; std::memcpy(dst, src, num); } @@ -125,6 +135,7 @@ void Copy( platform::CUDAPinnedPlace dst_place, void* dst, platform::CUDAPlace src_place, const void* src, size_t num, cudaStream_t stream) { + if (UNLIKELY(num == 0)) return; platform::SetDeviceId(src_place.device); if (stream) { platform::RecordEvent record_event("GpuMemcpyAsync:GPU->CUDAPinned"); @@ -140,6 +151,8 @@ void Copy( platform::CUDAPlace dst_place, void* dst, platform::CUDAPinnedPlace src_place, const void* src, size_t num, cudaStream_t stream) { + if (UNLIKELY(num == 0)) return; + platform::SetDeviceId(dst_place.device); if (stream) { platform::RecordEvent record_event("GpuMemcpyAsync:CUDAPinned->GPU"); diff --git a/paddle/fluid/op_use_default_grad_op_maker.spec b/paddle/fluid/op_use_default_grad_op_maker.spec index 403be1fc2c97a189a541c0c887eaadfe4266a124..a2355d2deee5784f85a65ba32bf1440a55fb6bed 100644 --- a/paddle/fluid/op_use_default_grad_op_maker.spec +++ b/paddle/fluid/op_use_default_grad_op_maker.spec @@ -29,7 +29,6 @@ prelu quantize rank_loss reduce_max -reduce_mean reduce_min reduce_prod reduce_sum diff --git a/paddle/fluid/operators/CMakeLists.txt b/paddle/fluid/operators/CMakeLists.txt index 6e8d6f459c51170c0f29542154aa3b1c0fd894f1..3356c1e669dd2f0bead5e5d096fef972cffe290a 100644 --- a/paddle/fluid/operators/CMakeLists.txt +++ b/paddle/fluid/operators/CMakeLists.txt @@ -8,7 +8,6 @@ file(WRITE ${pybind_file} "// Generated by the paddle/fluid/operator/CMakeLists. add_subdirectory(math) add_subdirectory(controlflow) -add_subdirectory(csp) add_subdirectory(detection) add_subdirectory(elementwise) add_subdirectory(fused) @@ -34,7 +33,7 @@ if (WITH_GPU AND TENSORRT_FOUND) add_subdirectory(tensorrt) endif() -if (ANAKIN_FOUND) +if (ANAKIN_SUBGRAPH) add_subdirectory(anakin) endif() @@ -48,7 +47,8 @@ if (WITH_DISTRIBUTE) SET(OP_PREFETCH_DEPS ${OP_PREFETCH_DEPS} parameter_prefetch) endif() -register_operators(EXCLUDES py_func_op warpctc_op dgc_op conv_fusion_op sync_batch_norm_op DEPS ${OP_HEADER_DEPS} ${OP_PREFETCH_DEPS}) +register_operators(EXCLUDES py_func_op warpctc_op dgc_op conv_fusion_op + sync_batch_norm_op deformable_conv_op DEPS ${OP_HEADER_DEPS} ${OP_PREFETCH_DEPS}) if (WITH_GPU) # warpctc_op needs cudnn 7 above @@ -66,6 +66,8 @@ if (WITH_GPU) op_library(sync_batch_norm_op) file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(sync_batch_norm);\n") endif() + op_library(deformable_conv_op) + file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(deformable_conv);\n") else() op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale) endif() diff --git a/paddle/fluid/operators/activation_op.cc b/paddle/fluid/operators/activation_op.cc index f93474a122f8f9f812750b94cf20c5c94b5b0823..66453027596108674dde90cc7f76b0f713501e55 100644 --- a/paddle/fluid/operators/activation_op.cc +++ b/paddle/fluid/operators/activation_op.cc @@ -597,40 +597,31 @@ REGISTER_ACTIVATION_OP_MAKER(Square, SquareDoc); REGISTER_ACTIVATION_OP_MAKER(Softplus, SoftplusDoc); REGISTER_ACTIVATION_OP_MAKER(Softsign, SoftsignDoc); +template class ActivationOpDoubleGrad : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - if (ctx->HasOutput("DOut")) { - ctx->ShareDim("Out", "DOut"); - ctx->ShareLoD("Out", "DOut"); - } - if (ctx->HasOutput("DDOut")) { - ctx->ShareDim("Out", "DDOut"); - ctx->ShareLoD("Out", "DDOut"); - } - } - - protected: - framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext& ctx) const override { - return GetKernelType(ctx, *this, "Out"); - } -}; - -class LeakyReluDoubleGrad : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - void InferShape(framework::InferShapeContext* ctx) const override { - if (ctx->HasOutput("DX")) { - ctx->ShareDim("X", "DX"); - ctx->ShareLoD("X", "DX"); + if (static_cast(kDepValue) & static_cast(kDepX)) { + if (HasOutputs("DX") && ctx->HasOutput("DX")) { + ctx->ShareDim("X", "DX"); + ctx->ShareLoD("X", "DX"); + } + if (HasOutputs("DDOut") && ctx->HasOutput("DDOut")) { + ctx->ShareDim("X", "DDOut"); + ctx->ShareLoD("X", "DDOut"); + } } - if (ctx->HasOutput("DDOut")) { - ctx->ShareDim("X", "DDOut"); - ctx->ShareLoD("X", "DDOut"); + if (static_cast(kDepValue) & static_cast(kDepOut)) { + if (HasOutputs("DOut") && ctx->HasOutput("DOut")) { + ctx->ShareDim("Out", "DOut"); + ctx->ShareLoD("Out", "DOut"); + } + if (HasOutputs("DDOut") && ctx->HasOutput("DDOut")) { + ctx->ShareDim("Out", "DDOut"); + ctx->ShareLoD("Out", "DDOut"); + } } } @@ -644,7 +635,6 @@ class LeakyReluDoubleGrad : public framework::OperatorWithKernel { // // ReluGrad: dx = dy if y >= 0 else 0 // ReluGradGrad: ddy = ddx if y >= 0 else 0 -// dy = 0 // class ReluDoubleGradMaker : public ::paddle::framework::SingleGradOpDescMaker { public: @@ -659,9 +649,7 @@ class ReluDoubleGradMaker : public ::paddle::framework::SingleGradOpDescMaker { // input2: ddx op->SetInput("DDX", OutputGrad(framework::GradVarName("X"))); op->SetAttrMap(Attrs()); - // output1: ddy - op->SetOutput("DOut", InputGrad("Out")); - // output2: ddy + // output: ddy op->SetOutput("DDOut", InputGrad(framework::GradVarName("Out"))); return std::unique_ptr<::paddle::framework::OpDesc>(op); } @@ -684,7 +672,53 @@ class LeakyReluDoubleGradMaker op->SetInput("DDX", OutputGrad(framework::GradVarName("X"))); op->SetAttrMap(Attrs()); // Out@GRAD@GRAD: ddy + op->SetOutput("DDOut", InputGrad(framework::GradVarName("Out"))); + return std::unique_ptr<::paddle::framework::OpDesc>(op); + } +}; + +// sqrt Grad: dx = 0.5 * dy / y +// sqrt GradGrad: ddy = 0.5 * ddx / y, dy = -1 * dx * ddx +class SqrtDoubleGradMaker : public ::paddle::framework::SingleGradOpDescMaker { + public: + using ::paddle::framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr<::paddle::framework::OpDesc> Apply() const override { + auto* op = new ::paddle::framework::OpDesc(); + op->SetType("sqrt_grad_grad"); + op->SetInput("Out", Input("Out")); + op->SetInput("DX", Output(framework::GradVarName("X"))); + op->SetInput("DDX", OutputGrad(framework::GradVarName("X"))); + op->SetAttrMap(Attrs()); + op->SetOutput("DOut", InputGrad("Out")); + op->SetOutput("DDOut", InputGrad(framework::GradVarName("Out"))); + return std::unique_ptr<::paddle::framework::OpDesc>(op); + } +}; + +// square Grad: dx=2x*dy +// square GradGrad: ddy=2x*ddx, dx=2dy*ddx +class SquareDoubleGradMaker + : public ::paddle::framework::SingleGradOpDescMaker { + public: + using ::paddle::framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr<::paddle::framework::OpDesc> Apply() const override { + auto* op = new ::paddle::framework::OpDesc(); + op->SetType("square_grad_grad"); + op->SetInput("X", Input("X")); + // Out@GRAD: dy + op->SetInput("DOut", Input(framework::GradVarName("Out"))); + // X@GRAD@GRAD: ddx + op->SetInput("DDX", OutputGrad(framework::GradVarName("X"))); + + op->SetAttrMap(Attrs()); + + // X@GRAD: dx op->SetOutput("DX", InputGrad("X")); + // Out@GRAD@GRAD: ddy op->SetOutput("DDOut", InputGrad(framework::GradVarName("Out"))); return std::unique_ptr<::paddle::framework::OpDesc>(op); } @@ -727,6 +761,7 @@ namespace plat = paddle::platform; FOR_EACH_ACTIVATION_OP(REGISTER_ACTIVATION_OP); FOR_EACH_ACTIVATION_OP(REGISTER_ACTIVATION_CPU_KERNEL); +/* ========================== relu register ============================= */ REGISTER_OPERATOR( relu, ops::ActivationOp, ops::ReluOpMaker, ops::ActivationOpInferVarType, ops::ActivationGradOpDescMaker::FwdDeps()>, @@ -734,7 +769,9 @@ REGISTER_OPERATOR( REGISTER_OPERATOR(relu_grad, ops::ActivationOpGrad, paddle::framework::SingleOpInplaceInToOut, ops::ReluDoubleGradMaker); -REGISTER_OPERATOR(relu_grad_grad, ops::ActivationOpDoubleGrad); +REGISTER_OPERATOR( + relu_grad_grad, + ops::ActivationOpDoubleGrad::FwdDeps()>); REGISTER_ACTIVATION_CPU_KERNEL(relu, Relu, ReluFunctor, ReluGradFunctor); @@ -746,7 +783,9 @@ REGISTER_OP_CPU_KERNEL( ops::ReluGradGradFunctor>, ops::ActivationDoubleGradKernel>); +/* ========================================================================== */ +/* ======================== leaky relu register ============================ */ REGISTER_OPERATOR( leaky_relu, ops::ActivationOp, ops::LeakyReluOpMaker, ops::ActivationOpInferVarType, @@ -755,7 +794,10 @@ REGISTER_OPERATOR( REGISTER_OPERATOR(leaky_relu_grad, ops::ActivationOpGrad, paddle::framework::SingleOpInplaceInToOut, ops::LeakyReluDoubleGradMaker); -REGISTER_OPERATOR(leaky_relu_grad_grad, ops::LeakyReluDoubleGrad); +REGISTER_OPERATOR( + leaky_relu_grad_grad, + ops::ActivationOpDoubleGrad::FwdDeps()>); + REGISTER_ACTIVATION_CPU_KERNEL(leaky_relu, LeakyRelu, LeakyReluFunctor, LeakyReluGradFunctor); REGISTER_OP_CPU_KERNEL( @@ -766,3 +808,51 @@ REGISTER_OP_CPU_KERNEL( ops::LeakyReluGradGradFunctor>, ops::ActivationDoubleGradKernel< plat::CPUDeviceContext, ops::LeakyReluGradGradFunctor>); +/* ========================================================================== */ + +/* =========================== sqrt register ============================= */ +REGISTER_OPERATOR( + sqrt, ops::ActivationOp, ops::SqrtOpMaker, ops::ActivationOpInferVarType, + ops::ActivationGradOpDescMaker::FwdDeps()>, + paddle::framework::SingleOpInplaceInToOut); +REGISTER_OPERATOR(sqrt_grad, ops::ActivationOpGrad, + paddle::framework::SingleOpInplaceInToOut, + ops::SqrtDoubleGradMaker); +REGISTER_OPERATOR( + sqrt_grad_grad, + ops::ActivationOpDoubleGrad::FwdDeps()>); +REGISTER_ACTIVATION_CPU_KERNEL(sqrt, Sqrt, SqrtFunctor, SqrtGradFunctor); +REGISTER_OP_CPU_KERNEL( + sqrt_grad_grad, ops::SqrtDoubleGradKernel>, + ops::SqrtDoubleGradKernel>, + ops::SqrtDoubleGradKernel>); +/* ========================================================================== */ + +/* ========================== square register ============================ */ +REGISTER_OPERATOR( + square, ops::ActivationOp, ops::SquareOpMaker, + ops::ActivationOpInferVarType, + ops::ActivationGradOpDescMaker::FwdDeps()>, + paddle::framework::SingleOpInplaceInToOut); +REGISTER_OPERATOR(square_grad, ops::ActivationOpGrad, + paddle::framework::SingleOpInplaceInToOut, + ops::SquareDoubleGradMaker); +REGISTER_OPERATOR( + square_grad_grad, + ops::ActivationOpDoubleGrad::FwdDeps()>); + +REGISTER_ACTIVATION_CPU_KERNEL(square, Square, SquareFunctor, + SquareGradFunctor); + +REGISTER_OP_CPU_KERNEL( + square_grad_grad, + ops::SquareDoubleGradKernel>, + ops::SquareDoubleGradKernel>, + ops::SquareDoubleGradKernel>); +/* ========================================================================== */ diff --git a/paddle/fluid/operators/activation_op.cu b/paddle/fluid/operators/activation_op.cu index 377e5a4af75d56abb4676fa5396051ce8b152bdf..25514186de9e424a46131e5b238b215c22911b38 100644 --- a/paddle/fluid/operators/activation_op.cu +++ b/paddle/fluid/operators/activation_op.cu @@ -33,6 +33,7 @@ namespace plat = paddle::platform; FOR_EACH_ACTIVATION_OP(REGISTER_ACTIVATION_CUDA_KERNEL); +/* ======================== leaky relu register ============================ */ REGISTER_ACTIVATION_CUDA_KERNEL(leaky_relu, LeakyRelu, LeakyReluFunctor, LeakyReluGradFunctor); @@ -44,7 +45,9 @@ REGISTER_OP_CUDA_KERNEL( ops::LeakyReluGradGradFunctor>, ops::ActivationDoubleGradKernel< plat::CUDADeviceContext, ops::LeakyReluGradGradFunctor>); +/* ========================================================================== */ +/* =========================== relu register ============================ */ REGISTER_ACTIVATION_CUDA_KERNEL(relu, Relu, ReluFunctor, ReluGradFunctor); REGISTER_OP_CUDA_KERNEL( @@ -55,3 +58,31 @@ REGISTER_OP_CUDA_KERNEL( ops::ReluGradGradFunctor>, ops::ActivationDoubleGradKernel>); +/* ========================================================================== */ + +/* =========================== sqrt register ============================= */ +REGISTER_ACTIVATION_CUDA_KERNEL(sqrt, Sqrt, SqrtFunctor, SqrtGradFunctor); + +REGISTER_OP_CUDA_KERNEL( + sqrt_grad_grad, + ops::SqrtDoubleGradKernel>, + ops::SqrtDoubleGradKernel>, + ops::SqrtDoubleGradKernel>); +/* ========================================================================== */ + +/* =========================== square register ============================ */ +REGISTER_ACTIVATION_CUDA_KERNEL(square, Square, SquareFunctor, + SquareGradFunctor); + +REGISTER_OP_CUDA_KERNEL( + square_grad_grad, + ops::SquareDoubleGradKernel>, + ops::SquareDoubleGradKernel>, + ops::SquareDoubleGradKernel>); +/* ========================================================================== */ diff --git a/paddle/fluid/operators/activation_op.h b/paddle/fluid/operators/activation_op.h index 5848d9dad5e995eec51f54ae278d997e59195e1d..b516fc8a418599d429e47748f53e8a6ed1f65624 100644 --- a/paddle/fluid/operators/activation_op.h +++ b/paddle/fluid/operators/activation_op.h @@ -1,4 +1,5 @@ /* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -1320,10 +1321,6 @@ struct ReluGradGradFunctor : public BaseActivationFunctor { auto ddout = framework::EigenVector::Flatten(detail::Ref(ddOut)); ddout.device(*d) = ddx * (out > static_cast(0)).template cast(); } - if (dOut) { - auto dout = framework::EigenVector::Flatten(detail::Ref(dOut)); - dout.device(*d) = dout.constant(static_cast(0)); - } } static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; } }; @@ -1350,14 +1347,171 @@ struct LeakyReluGradGradFunctor : public BaseActivationFunctor { (x < static_cast(0)).template cast().eval()) .template cast(); } + } + static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; } +}; + +template +struct SqrtGradGradFunctor : public BaseActivationFunctor { + template + void operator()(const Device& dev, const framework::Tensor* Out, + const framework::Tensor* ddX, framework::Tensor* ddOut, + framework::Tensor* dOut, const framework::Tensor* dX) const { + auto* d = dev.eigen_device(); + auto ddx = framework::EigenVector::Flatten(detail::Ref(ddX)); + auto out = framework::EigenVector::Flatten(detail::Ref(Out)); + if (ddOut) { + auto ddout = framework::EigenVector::Flatten(detail::Ref(ddOut)); + ddout.device(*d) = ddx * static_cast(0.5) / out; + } + if (dOut) { + auto dx = framework::EigenVector::Flatten(detail::Ref(dX)); + auto dout = framework::EigenVector::Flatten(detail::Ref(dOut)); + dout.device(*d) = dx * ddx * static_cast(-1) / out; + } + } + static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepOut; } +}; + +template +struct SquareGradGradFunctor : public BaseActivationFunctor { + template + void operator()(const Device& dev, const framework::Tensor* X, + const framework::Tensor* ddX, framework::Tensor* ddOut, + const framework::Tensor* dOut, framework::Tensor* dX) const { + auto* d = dev.eigen_device(); + auto ddx = framework::EigenVector::Flatten(detail::Ref(ddX)); + auto x = framework::EigenVector::Flatten(detail::Ref(X)); + if (ddOut) { + auto ddout = framework::EigenVector::Flatten(detail::Ref(ddOut)); + ddout.device(*d) = ddx * static_cast(2) * x; + } if (dX) { auto dx = framework::EigenVector::Flatten(detail::Ref(dX)); - dx.device(*d) = dx.constant(static_cast(0)); + auto dout = framework::EigenVector::Flatten(detail::Ref(dOut)); + dx.device(*d) = ddx * static_cast(2) * dout; } } static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; } }; +// TODO(dengkaipeng): double gradient calculation for Square/Sqrt need +// DOut(dy) as input(not output), tensor extraction is different from +// others. Impliment extraction kernel seperately here. +inline void ExtractDoubleGradTensorWithInputDOut( + const framework::ExecutionContext& ctx, const framework::Tensor** X, + const framework::Tensor** ddX, framework::Tensor** dX, + const framework::Tensor** dOut, framework::Tensor** ddOut) { + // extract ddX(output), ddOut(input) + auto ddx_var = ctx.InputVar("DDX"); + auto ddo_var = ctx.OutputVar("DDOut"); + PADDLE_ENFORCE(ddx_var != nullptr, + "Cannot get input Variable Out, variable name = %s", + ctx.op().Input("DDX")); + *ddX = ctx.Input("DDX"); + if (ddo_var) { + *ddOut = ctx.Output("DDOut"); + } + PADDLE_ENFORCE(*ddX != nullptr, + "Cannot get output tensor DDX, variable name = %s", + ctx.op().Output("DDX")); + + // extract x(input), dx(output) + auto x_var = ctx.InputVar("X"); + PADDLE_ENFORCE(x_var != nullptr, + "Cannot get input Variable Out, variable name = %s", + ctx.op().Input("X")); + auto dx_var = ctx.OutputVar("DX"); + *X = ctx.Input("X"); + if (dx_var) { + *dX = ctx.Output("DX"); + } + + // extract dOut(input) + auto dout_var = ctx.InputVar("DOut"); + if (dout_var) { + *dOut = ctx.Input("DOut"); + } +} + +template +class SquareDoubleGradKernel + : public framework::OpKernel { + public: + using T = typename Functor::ELEMENT_TYPE; + void Compute(const framework::ExecutionContext& ctx) const override { + const framework::Tensor *X, *ddX, *dOut; + X = ddX = dOut = nullptr; + framework::Tensor *dX, *ddOut; + dX = ddOut = nullptr; + + ExtractDoubleGradTensorWithInputDOut(ctx, &X, &ddX, &dX, &dOut, &ddOut); + + if (dX) dX->mutable_data(X->dims(), ctx.GetPlace()); + if (ddOut) ddOut->mutable_data(ctx.GetPlace()); + + auto& place = ctx.template device_context(); + + Functor functor; + functor(place, X, ddX, ddOut, dOut, dX); + } +}; + +template +class SqrtDoubleGradKernel + : public framework::OpKernel { + public: + using T = typename Functor::ELEMENT_TYPE; + void Compute(const framework::ExecutionContext& ctx) const override { + const framework::Tensor *Out, *dX, *ddX; + Out = dX = ddX = nullptr; + framework::Tensor *ddOut, *dOut; + ddOut = dOut = nullptr; + + // extract ddx(input), ddout(output) + auto ddx_var = ctx.InputVar("DDX"); + auto ddo_var = ctx.OutputVar("DDOut"); + PADDLE_ENFORCE(ddx_var != nullptr, + "Cannot get input Variable DDX, variable name = %s", + ctx.op().Input("DDX")); + ddX = ctx.Input("DDX"); + if (ddo_var) { + ddOut = ctx.Output("DDOut"); + } + PADDLE_ENFORCE(ddX != nullptr, + "Cannot get input Variable DDX, variable name = %s", + ctx.op().Input("DDX")); + + // extract out(input), dout(output) + auto out_var = ctx.InputVar("Out"); + PADDLE_ENFORCE(out_var != nullptr, + "Cannot get input Variable Out, variable name = %s", + ctx.op().Input("Out")); + auto dout_var = ctx.OutputVar("DOut"); + Out = ctx.Input("Out"); + if (dout_var) { + dOut = ctx.Output("DOut"); + } + + // extract dx(input) + auto dx_var = ctx.InputVar("DX"); + PADDLE_ENFORCE(dx_var != nullptr, + "Cannot get input Variable DX, variable name = %s", + ctx.op().Input("DX")); + if (dx_var) { + dX = ctx.Input("DX"); + } + + if (dOut) dOut->mutable_data(Out->dims(), ctx.GetPlace()); + if (ddOut) ddOut->mutable_data(Out->dims(), ctx.GetPlace()); + + auto& place = ctx.template device_context(); + + Functor functor; + functor(place, Out, ddX, ddOut, dOut, dX); + } +}; + } // namespace operators } // namespace paddle @@ -1369,7 +1523,6 @@ struct LeakyReluGradGradFunctor : public BaseActivationFunctor { __macro(tanh, Tanh, TanhFunctor, TanhGradFunctor); \ __macro(atan, Atan, AtanFunctor, AtanGradFunctor); \ __macro(softshrink, SoftShrink, SoftShrinkFunctor, SoftShrinkGradFunctor); \ - __macro(sqrt, Sqrt, SqrtFunctor, SqrtGradFunctor); \ __macro(rsqrt, Rsqrt, RsqrtFunctor, RsqrtGradFunctor); \ __macro(abs, Abs, AbsFunctor, AbsGradFunctor); \ __macro(ceil, Ceil, CeilFunctor, ZeroGradFunctor); \ @@ -1381,7 +1534,6 @@ struct LeakyReluGradGradFunctor : public BaseActivationFunctor { __macro(round, Round, RoundFunctor, ZeroGradFunctor); \ __macro(reciprocal, Reciprocal, ReciprocalFunctor, ReciprocalGradFunctor); \ __macro(log, Log, LogFunctor, LogGradFunctor); \ - __macro(square, Square, SquareFunctor, SquareGradFunctor); \ __macro(brelu, BRelu, BReluFunctor, BReluGradFunctor); \ __macro(soft_relu, SoftRelu, SoftReluFunctor, SoftReluGradFunctor); \ __macro(pow, Pow, PowFunctor, PowGradFunctor); \ diff --git a/paddle/fluid/operators/add_position_encoding_op.cc b/paddle/fluid/operators/add_position_encoding_op.cc index 3882bbedaa0be0ba14bca9c4fcb626d5ecaab129..2580c5a523e13fb489bf9810c205257102d8a72e 100644 --- a/paddle/fluid/operators/add_position_encoding_op.cc +++ b/paddle/fluid/operators/add_position_encoding_op.cc @@ -33,6 +33,13 @@ class AddPositionEncodingOp : public framework::OperatorWithKernel { ctx->SetOutputDim("Out", x_dims); ctx->ShareLoD("X", /*->*/ "Out"); } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + return framework::OpKernelType(ctx.Input("X")->type(), + platform::CPUPlace()); + } }; class AddPositionEncodingOpGrad : public framework::OperatorWithKernel { @@ -45,6 +52,14 @@ class AddPositionEncodingOpGrad : public framework::OperatorWithKernel { ctx->SetOutputDim(framework::GradVarName("X"), out_dims); } } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + return framework::OpKernelType( + ctx.Input(framework::GradVarName("Out"))->type(), + platform::CPUPlace()); + } }; class AddPositionEncodingOpMaker : public framework::OpProtoAndCheckerMaker { diff --git a/paddle/fluid/operators/alloc_continuous_space_op.cc b/paddle/fluid/operators/alloc_continuous_space_op.cc index d4bdecff62c016a31011266a0f066076d85fcdef..85da8a827f715456340cb9d0ba689235ea47095a 100644 --- a/paddle/fluid/operators/alloc_continuous_space_op.cc +++ b/paddle/fluid/operators/alloc_continuous_space_op.cc @@ -12,6 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include #include #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/operator.h" @@ -96,6 +97,8 @@ class AllocContinuousSpaceKernel : public framework::OpKernel { // Make the outputs point to the continuous space. offset = 0; + std::stringstream ss; + ss << "alloc_space_for_vars: "; for (size_t i = 0; i < out_tensors.size(); ++i) { size_t len = static_cast(out_tensors[i]->numel()); auto dim = out_tensors[i]->dims(); @@ -105,10 +108,10 @@ class AllocContinuousSpaceKernel : public framework::OpKernel { .Resize(dim); len = Alignment(len * size_of_dtype, context.GetPlace()) / size_of_dtype; offset += len; - VLOG(10) << "alloc_space_for_vars: output(" << out_var_names[i] - << ") ,dim:(" << dim << ")" - << " Address: " << out_tensors[i]->data(); + ss << "output(" << out_var_names[i] << ") dim:(" << dim << ")" + << " address: " << out_tensors[i]->data() << ", "; } + VLOG(10) << ss.str(); } private: @@ -133,6 +136,9 @@ class AllocContinuousSpaceKernel : public framework::OpKernel { PADDLE_ENFORCE_EQ(lod_tensors.size(), var_names.size()); *numel = 0; size_t size_of_dtype = 0; + + std::stringstream ss; + ss << "alloc_space_for_vars: "; for (size_t i = 0; i < var_names.size(); ++i) { PADDLE_ENFORCE(lod_tensors[i]->IsInitialized(), "%s is not initialized.", var_names[i]); @@ -148,11 +154,13 @@ class AllocContinuousSpaceKernel : public framework::OpKernel { auto size = lod_tensors[i]->numel(); PADDLE_ENFORCE_GT(size, 0); - VLOG(10) << "alloc_space_for_vars: input(" << var_names[i] << ") ,dim:(" - << lod_tensors[i]->dims() << ")"; + ss << "input(" << var_names[i] << ") dim:(" << lod_tensors[i]->dims() + << "), "; *numel += Alignment(static_cast(size) * size_of_dtype, place) / size_of_dtype; } + + VLOG(10) << ss.str(); } }; diff --git a/paddle/fluid/operators/anakin/anakin_engine_op.h b/paddle/fluid/operators/anakin/anakin_engine_op.h index 11c394c76cd9828d4ff84712a23236dfc8f919e0..b4aaa228693c8f438a2df3dd316f68b2acaafcc2 100644 --- a/paddle/fluid/operators/anakin/anakin_engine_op.h +++ b/paddle/fluid/operators/anakin/anakin_engine_op.h @@ -119,11 +119,15 @@ class AnakinEngineOp : public framework::OperatorBase { engine->Execute(inputs, outputs, stream); #endif } else { +#ifdef ANAKIN_X86_PLACE auto *engine = inference::Singleton>::Global() .Get(engine_key_); engine->Execute(inputs, outputs); +#else + LOG(FATAL) << "Unknown Platform for AnakinEngine!"; +#endif } } }; diff --git a/paddle/fluid/operators/batch_norm_op.cc b/paddle/fluid/operators/batch_norm_op.cc index d583909a666624d86031bb207154c93cf12d5cc2..f6295337d1f1042f021f7b0de15f476225beb3a2 100644 --- a/paddle/fluid/operators/batch_norm_op.cc +++ b/paddle/fluid/operators/batch_norm_op.cc @@ -454,6 +454,7 @@ class BatchNormGradKernel const auto *running_mean = ctx.Input("Mean"); const auto *running_variance = ctx.Input("Variance"); mean_data = running_mean->data(); + inv_var_tensor.Resize({C}); T *running_inv_var_data = inv_var_tensor.mutable_data(ctx.GetPlace()); EigenVectorArrayMap inv_var_tmp(running_inv_var_data, C); ConstEigenVectorArrayMap var_arr(running_variance->data(), C); diff --git a/paddle/fluid/operators/benchmark/op_tester.cc b/paddle/fluid/operators/benchmark/op_tester.cc index fec091255f6391b77cd2858905f3aa2e5dd8baff..ac487223d09b1b5be2cb889fb7fb7f60c0093397 100644 --- a/paddle/fluid/operators/benchmark/op_tester.cc +++ b/paddle/fluid/operators/benchmark/op_tester.cc @@ -257,7 +257,8 @@ framework::VarDesc *OpTester::Var(const std::string &name) { template void OpTester::SetupTensor(framework::LoDTensor *tensor, const std::vector &shape, T lower, T upper, - const std::string &initializer) { + const std::string &initializer, + const std::string &filename) { static unsigned int seed = 100; std::mt19937 rng(seed++); std::uniform_real_distribution uniform_dist(0, 1); @@ -280,12 +281,20 @@ void OpTester::SetupTensor(framework::LoDTensor *tensor, } } else if (initializer == "natural") { for (int i = 0; i < cpu_tensor.numel(); ++i) { - cpu_ptr[i] = lower + i; + cpu_ptr[i] = static_cast(lower + i); } } else if (initializer == "zeros") { for (int i = 0; i < cpu_tensor.numel(); ++i) { - cpu_ptr[i] = 0; + cpu_ptr[i] = static_cast(0); } + } else if (initializer == "file") { + std::ifstream is(filename); + for (size_t i = 0; i < cpu_tensor.numel(); ++i) { + T value; + is >> value; + cpu_ptr[i] = static_cast(value); + } + is.close(); } else { PADDLE_THROW("Unsupported initializer %s.", initializer.c_str()); } @@ -325,15 +334,19 @@ void OpTester::CreateVariables(framework::Scope *scope) { auto *tensor = var->GetMutable(); const auto &data_type = var_desc->GetDataType(); if (data_type == framework::proto::VarType::INT32) { - SetupTensor(tensor, shape, 0, 1, item.second.initializer); + SetupTensor(tensor, shape, 0, 1, item.second.initializer, + item.second.filename); } else if (data_type == framework::proto::VarType::INT64) { - SetupTensor(tensor, shape, 0, 1, item.second.initializer); + SetupTensor(tensor, shape, 0, 1, item.second.initializer, + item.second.filename); } else if (data_type == framework::proto::VarType::FP32) { SetupTensor(tensor, shape, static_cast(0.0), - static_cast(1.0), item.second.initializer); + static_cast(1.0), item.second.initializer, + item.second.filename); } else if (data_type == framework::proto::VarType::FP64) { SetupTensor(tensor, shape, static_cast(0.0), - static_cast(1.0), item.second.initializer); + static_cast(1.0), item.second.initializer, + item.second.filename); } else { PADDLE_THROW("Unsupported dtype %d.", data_type); } diff --git a/paddle/fluid/operators/benchmark/op_tester.h b/paddle/fluid/operators/benchmark/op_tester.h index 328389293c4b71a2f1fefbc3bf26fd46b79ec6e2..a6d21573a05166a5cb98e78d4993f9304882d2e1 100644 --- a/paddle/fluid/operators/benchmark/op_tester.h +++ b/paddle/fluid/operators/benchmark/op_tester.h @@ -55,7 +55,7 @@ class OpTester { template void SetupTensor(framework::LoDTensor *input, const std::vector &shape, T lower, T upper, - const std::string &initializer); + const std::string &initializer, const std::string &filename); void RunImpl(); diff --git a/paddle/fluid/operators/benchmark/op_tester_config.cc b/paddle/fluid/operators/benchmark/op_tester_config.cc index b4878ab04244cf6b54d323943fc1fbf4e3882660..818e5f64edc2c1d213659c48d282df75625676ca 100644 --- a/paddle/fluid/operators/benchmark/op_tester_config.cc +++ b/paddle/fluid/operators/benchmark/op_tester_config.cc @@ -56,6 +56,9 @@ OpInputConfig::OpInputConfig(std::istream& is) { ParseDims(is); } else if (sep == "lod" || sep == "lod:") { ParseLoD(is); + } else if (sep == "filename") { + is >> filename; + EraseEndSep(&filename); } } } @@ -86,7 +89,7 @@ void OpInputConfig::ParseInitializer(std::istream& is) { EraseEndSep(&initializer_str); const std::vector supported_initializers = {"random", "natural", - "zeros"}; + "zeros", "file"}; if (!Has(supported_initializers, initializer_str)) { PADDLE_THROW("Unsupported initializer %s", initializer_str.c_str()); } diff --git a/paddle/fluid/operators/benchmark/op_tester_config.h b/paddle/fluid/operators/benchmark/op_tester_config.h index 5803f82ac28867a481875c2af607290c5d366146..3956bc0a8b1080e14cb773c9664f821dc7e40abd 100644 --- a/paddle/fluid/operators/benchmark/op_tester_config.h +++ b/paddle/fluid/operators/benchmark/op_tester_config.h @@ -35,7 +35,8 @@ struct OpInputConfig { std::string name; std::string dtype{"fp32"}; // int32/int, int64/long, fp32/float, fp64/double - std::string initializer{"random"}; // random, natural + std::string initializer{"random"}; // random, natural, zeros, file + std::string filename{""}; std::vector dims; std::vector> lod; }; diff --git a/paddle/fluid/operators/concat_op.cc b/paddle/fluid/operators/concat_op.cc index 029b05bb662440bcf94521376b56d234a828ddf5..7f249924f5b9a1092af725f2f9271ac3cdbd26f3 100644 --- a/paddle/fluid/operators/concat_op.cc +++ b/paddle/fluid/operators/concat_op.cc @@ -23,7 +23,7 @@ limitations under the License. */ namespace paddle { namespace operators { -using framework::Tensor; +using Tensor = framework::Tensor; class ConcatOp : public framework::OperatorWithKernel { public: @@ -36,7 +36,10 @@ class ConcatOp : public framework::OperatorWithKernel { "Output(Out) of ConcatOp should not be null."); auto ins = ctx->GetInputsDim("X"); - size_t axis = static_cast(ctx->Attrs().Get("axis")); + size_t axis = + ComputeAxis(static_cast(ctx->Attrs().Get("axis")), + static_cast(ins[0].size())); + const size_t n = ins.size(); PADDLE_ENFORCE_GT(n, 0, "Input tensors count should > 0."); @@ -80,8 +83,19 @@ class ConcatOp : public framework::OperatorWithKernel { protected: framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext &ctx) const override { - auto input_data_type = - framework::GetDataTypeOfVar(ctx.MultiInputVar("X")[0]); + auto inputs = ctx.MultiInput("X"); + auto input_data_type = framework::proto::VarType::Type(0); + bool flag = 0; + for (auto *input : inputs) { + if (input->IsInitialized() && input->numel() > 0) { + input_data_type = input->type(); + flag = 1; + break; + } + } + if (flag == 0) { + PADDLE_THROW("All Inputs of Concat OP are Empty!"); + } #ifdef PADDLE_WITH_MKLDNN if (platform::CanMKLDNNBeUsed(ctx)) { @@ -104,8 +118,17 @@ class ConcatOpMaker : public framework::OpProtoAndCheckerMaker { "(bool, default false) Indicates if MKL-DNN kernel will be used") .SetDefault(false); AddAttr("axis", - "The axis along which the input tensors will be concatenated.") + "The axis along which the input tensors will be concatenated." + "The axis could also be negative numbers. Negative axis is " + "interpreted as counting from the end of the rank." + "i.e., axis + rank(X) th dimension.") .SetDefault(0); + AddAttr("use_quantizer", + "(bool, default false) " + "Set to true for operators that should be quantized and use " + "int8 kernel. " + "Only used on CPU.") + .SetDefault(false); AddComment(R"DOC( Concat Operator. diff --git a/paddle/fluid/operators/concat_op.h b/paddle/fluid/operators/concat_op.h index 0414550dd18f7818ff922dfd5113ede763299185..4a371de32354d196492a54dce47bf73bf644bad1 100644 --- a/paddle/fluid/operators/concat_op.h +++ b/paddle/fluid/operators/concat_op.h @@ -23,13 +23,22 @@ limitations under the License. */ namespace paddle { namespace operators { +static inline int64_t ComputeAxis(int64_t axis, int64_t rank) { + if (axis < 0) { + axis = axis + rank; + } + return axis > 0 ? axis : 0; +} + template class ConcatKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto ins = ctx.MultiInput("X"); framework::Tensor* out = ctx.Output("Out"); - int64_t axis = static_cast(ctx.Attr("axis")); + PADDLE_ENFORCE(ins[0], "The input should not be null."); + auto axis = ComputeAxis(static_cast(ctx.Attr("axis")), + static_cast(ins[0]->dims().size())); auto place = ctx.GetPlace(); out->mutable_data(place); @@ -83,8 +92,9 @@ class ConcatGradKernel : public framework::OpKernel { } } } - - int64_t axis = static_cast(ctx.Attr("axis")); + PADDLE_ENFORCE(ins[0], "The input should not be null."); + auto axis = ComputeAxis(static_cast(ctx.Attr("axis")), + static_cast(ins[0]->dims().size())); // get output tensor that the name is not kEmptyVarName std::vector outputs; diff --git a/paddle/fluid/operators/conv_cudnn_op.cu.cc b/paddle/fluid/operators/conv_cudnn_op.cu.cc index 158d6ced274dd33b1378403b325e736037fc042d..054deeaa710c8e058118b33662a15542678bf961 100644 --- a/paddle/fluid/operators/conv_cudnn_op.cu.cc +++ b/paddle/fluid/operators/conv_cudnn_op.cu.cc @@ -136,7 +136,7 @@ class CUDNNConvOpKernel : public framework::OpKernel { } // ------------------- cudnn conv algorithm --------------------- - cudnnConvolutionFwdAlgo_t algo; + cudnnConvolutionFwdAlgo_t algo{}; bool half_float = false; #if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1) @@ -165,11 +165,43 @@ class CUDNNConvOpKernel : public framework::OpKernel { // TODO(dangqingqing) simplify the following code by SearchAlgorithm in // conv_cudnn_helper.h + bool has_got_workspace_size = false; if ((!exhaustive_search) && (!half_float)) { - CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm( +#if CUDNN_VERSION >= 7001 + using perf_t = cudnnConvolutionFwdAlgoPerf_t; + int perf_count; + int best_algo_idx = 0; + std::unique_ptr perf_results(new perf_t[kNUM_CUDNN_FWD_ALGS]); + CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm_v7( handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc, - cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, - workspace_size_limit, &algo)); + cudnn_output_desc, kNUM_CUDNN_FWD_ALGS, &perf_count, + perf_results.get())); + algo = (perf_results.get())[best_algo_idx].algo; + + // get workspace size able to allocate + CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize( + handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc, + cudnn_output_desc, algo, &workspace_size_in_bytes)); + + // NOTE(zjl): cudnnGetConvolutionForwardAlgorithm_v7 cannot limit + // workspace size. If the workspace size found by v7 exceeds the limit, + // we should fallback to non-v7 method to find another algorithm. + if (workspace_size_in_bytes > workspace_size_limit) { + VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue " + "the workspace size request(" + << workspace_size_in_bytes << ") exceeds the limit(" + << workspace_size_limit << ")"; +#endif + CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm( + handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc, + cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, + workspace_size_limit, &algo)); +#if CUDNN_VERSION >= 7001 + } else { + has_got_workspace_size = true; + } +#endif + VLOG(3) << "cuDNN forward algo " << algo; } else if (exhaustive_search && (!half_float)) { AlgorithmsCache& algo_cache = @@ -206,10 +238,13 @@ class CUDNNConvOpKernel : public framework::OpKernel { "cuDNN exhaustive search doesn't support half float."); } - // get workspace size able to allocate - CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize( - handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc, - cudnn_output_desc, algo, &workspace_size_in_bytes)); + if (!has_got_workspace_size) { + // get workspace size able to allocate + CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize( + handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc, + cudnn_output_desc, algo, &workspace_size_in_bytes)); + } + // It is possible for float16 on Volta GPU to allocate more memory than // the limit because the algo is overrided to use tensor core. PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit, @@ -326,8 +361,8 @@ class CUDNNConvGradOpKernel : public framework::OpKernel { int group_offset_out = o_c / groups * o_h * o_w * o_d; int group_offset_filter = filter->numel() / groups; // ------------------- cudnn backward algorithm --------------------- - cudnnConvolutionBwdDataAlgo_t data_algo; - cudnnConvolutionBwdFilterAlgo_t filter_algo; + cudnnConvolutionBwdDataAlgo_t data_algo{}; + cudnnConvolutionBwdFilterAlgo_t filter_algo{}; size_t workspace_size_in_bytes = 0, tmp_size = 0; size_t workspace_size_limit = 0; if (FLAGS_conv_workspace_size_limit > 0 || user_workspace_size > 0) { @@ -353,6 +388,8 @@ class CUDNNConvGradOpKernel : public framework::OpKernel { auto x_dims = framework::vectorize(input->dims()); auto f_dims = framework::vectorize(filter->dims()); auto handle = dev_ctx.cudnn_handle(); + + bool has_got_bwd_data_ws_size = false; if (input_grad) { T* input_grad_data = input_grad->mutable_data(ctx.GetPlace()); if (exhaustive_search) { @@ -388,8 +425,14 @@ class CUDNNConvGradOpKernel : public framework::OpKernel { } else if (FLAGS_cudnn_deterministic) { data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1; } else { +#if CUDNN_VERSION >= 7001 + using perf_t = cudnnConvolutionBwdDataAlgoPerf_t; + int perf_count; + int best_algo_idx = 0; + std::unique_ptr perf_results( + new perf_t[kNUM_CUDNN_BWD_DATA_ALGS]); CUDNN_ENFORCE( - platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm( + platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm_v7( handle, cudnn_filter_desc, // dyDesc: Handle to the previously initialized input // differential @@ -397,17 +440,64 @@ class CUDNNConvGradOpKernel : public framework::OpKernel { cudnn_output_grad_desc, cudnn_conv_desc, // dxDesc: Handle to the previously initialized output tensor // descriptor. - cudnn_input_desc, - CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, - workspace_size_limit, &data_algo)); + cudnn_input_desc, kNUM_CUDNN_BWD_DATA_ALGS, &perf_count, + perf_results.get())); + data_algo = (perf_results.get())[best_algo_idx].algo; + int stride_dim = input->dims().size() - 2; + bool blacklist = + std::any_of(strides.begin(), strides.begin() + stride_dim, + [=](int n) { return n != 1; }); + if (blacklist && (static_cast( + perf_results[best_algo_idx].algo) == + CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING || + static_cast( + perf_results[best_algo_idx].algo) == + CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT)) { + data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1; + } + + CUDNN_ENFORCE( + platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize( + handle, cudnn_filter_desc, cudnn_output_grad_desc, + cudnn_conv_desc, cudnn_input_desc, data_algo, &tmp_size)); + auto new_workspace_size = std::max(workspace_size_in_bytes, tmp_size); + + if (new_workspace_size > workspace_size_limit) { + VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue " + "the workspace size request(" + << new_workspace_size << ") exceeds the limit(" + << workspace_size_limit << ")"; +#endif + CUDNN_ENFORCE( + platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm( + handle, cudnn_filter_desc, + // dyDesc: Handle to the previously initialized input + // differential + // tensor descriptor. + cudnn_output_grad_desc, cudnn_conv_desc, + // dxDesc: Handle to the previously initialized output tensor + // descriptor. + cudnn_input_desc, + CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, + workspace_size_limit, &data_algo)); +#if CUDNN_VERSION >= 7001 + } else { + workspace_size_in_bytes = new_workspace_size; + has_got_bwd_data_ws_size = true; + } +#endif + } + + if (!has_got_bwd_data_ws_size) { + CUDNN_ENFORCE( + platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize( + handle, cudnn_filter_desc, cudnn_output_grad_desc, + cudnn_conv_desc, cudnn_input_desc, data_algo, &tmp_size)); + workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size); } - CUDNN_ENFORCE( - platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize( - handle, cudnn_filter_desc, cudnn_output_grad_desc, - cudnn_conv_desc, cudnn_input_desc, data_algo, &tmp_size)); - workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size); } + bool has_got_bwd_filter_ws_size = false; if (filter_grad) { T* filter_grad_data = filter_grad->mutable_data(ctx.GetPlace()); if (exhaustive_search) { @@ -437,20 +527,58 @@ class CUDNNConvGradOpKernel : public framework::OpKernel { } else if (FLAGS_cudnn_deterministic) { filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1; } else { +#if CUDNN_VERSION >= 7001 + using perf_t = cudnnConvolutionBwdFilterAlgoPerf_t; + int perf_count; + int best_algo_idx = 0; + std::unique_ptr perf_results( + new perf_t[kNUM_CUDNN_BWD_FILTER_ALGS]); + CUDNN_ENFORCE( - platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm( + platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm_v7( handle, cudnn_input_desc, cudnn_output_grad_desc, - cudnn_conv_desc, cudnn_filter_desc, - CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, - workspace_size_limit, &filter_algo)); + cudnn_conv_desc, cudnn_filter_desc, kNUM_CUDNN_BWD_FILTER_ALGS, + &perf_count, perf_results.get())); + filter_algo = (perf_results.get())[best_algo_idx].algo; + + CUDNN_ENFORCE( + platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize( + handle, cudnn_input_desc, cudnn_output_grad_desc, + cudnn_conv_desc, cudnn_filter_desc, filter_algo, &tmp_size)); + auto new_workspace_size = std::max(workspace_size_in_bytes, tmp_size); + + if (new_workspace_size > workspace_size_limit) { + VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue " + "the workspace size request(" + << new_workspace_size << ") exceeds the limit(" + << workspace_size_limit << ")"; +#endif + CUDNN_ENFORCE( + platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm( + handle, cudnn_input_desc, cudnn_output_grad_desc, + cudnn_conv_desc, cudnn_filter_desc, + CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, + workspace_size_limit, &filter_algo)); +#if CUDNN_VERSION >= 7001 + } else { + workspace_size_in_bytes = new_workspace_size; + has_got_bwd_filter_ws_size = true; + } +#endif + } + + if (!has_got_bwd_filter_ws_size) { + CUDNN_ENFORCE( + platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize( + handle, cudnn_input_desc, cudnn_output_grad_desc, + cudnn_conv_desc, cudnn_filter_desc, filter_algo, &tmp_size)); + workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size); } - CUDNN_ENFORCE( - platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize( - handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc, - cudnn_filter_desc, filter_algo, &tmp_size)); - workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size); } + PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit, + "workspace_size to be allocated exceeds the limit"); + // ------------------- cudnn conv workspace --------------------- if (!cudnn_workspace_ptr) { cudnn_workspace = diff --git a/paddle/fluid/operators/conv_op.cc b/paddle/fluid/operators/conv_op.cc index 5b923f8a5eb58cfdf5809c677dfc915a68c64aae..ee37585a709f3068fadb2336a81e8b15b1c083a2 100644 --- a/paddle/fluid/operators/conv_op.cc +++ b/paddle/fluid/operators/conv_op.cc @@ -209,6 +209,12 @@ void Conv2DOpMaker::Make() { .SetDefault(false); AddAttr("fuse_relu", "(bool, default false) Only used in mkldnn kernel") .SetDefault(false); + AddAttr("fuse_brelu", + "(bool, default false) Only used in mkldnn kernel") + .SetDefault(false); + AddAttr("fuse_brelu_threshold", + "(float, default false 6.0) Only used in mkldnn kernel") + .SetDefault(6.0f); AddAttr("fuse_residual_connection", "(bool, default false) Only used in mkldnn kernel. Used " "whenever convolution output is as an input to residual " @@ -527,9 +533,16 @@ class Conv2DDoubleGradMaker : public framework::SingleGradOpDescMaker { // ddO, dI, dW // Unlike grad op, double grad op does not use name@GRAD@GRAD // as key of ops' inputs and outputs. - op->SetOutput("DDOutput", InputGrad(framework::GradVarName("Output"))); - op->SetOutput("DFilter", InputGrad("Filter")); - op->SetOutput("DInput", InputGrad("Input")); + auto ddx = OutputGrad(framework::GradVarName("Input")); + auto ddw = OutputGrad(framework::GradVarName("Filter")); + std::vector empty_str = {}; + + op->SetOutput( + "DDOutput", + ddx.empty() ? empty_str : InputGrad(framework::GradVarName("Output"))); + op->SetOutput("DFilter", ddx.empty() ? empty_str : InputGrad("Filter")); + op->SetOutput("DInput", ddw.empty() ? empty_str : InputGrad("Input")); + op->SetAttrMap(Attrs()); return std::unique_ptr(op); @@ -541,13 +554,13 @@ void ConvOpDoubleGrad::InferShape(framework::InferShapeContext* ctx) const { auto w_dims = ctx->GetInputDim("Filter"); auto do_dims = ctx->GetInputDim("DOutput"); - if (ctx->HasOutput("DDOutput")) { + if (ctx->HasOutput("DDOutput") && ctx->HasInput("DDInput")) { ctx->SetOutputDim("DDOutput", do_dims); } - if (ctx->HasOutput("DFilter")) { + if (ctx->HasOutput("DFilter") && ctx->HasInput("DDInput")) { ctx->SetOutputDim("DFilter", w_dims); } - if (ctx->HasOutput("DInput")) { + if (ctx->HasOutput("DInput") && ctx->HasInput("DDFilter")) { ctx->SetOutputDim("DInput", x_dims); } } diff --git a/paddle/fluid/operators/cross_entropy_op.h b/paddle/fluid/operators/cross_entropy_op.h index 89bacfc33edceb77017aad599c081710f4d4db33..309ba46cfa3b35fd4f6a4a889965b717b890a303 100644 --- a/paddle/fluid/operators/cross_entropy_op.h +++ b/paddle/fluid/operators/cross_entropy_op.h @@ -154,9 +154,9 @@ struct HardLabelCrossEntropyForwardFunctor { HOSTDEVICE void operator()(int64_t idx) const { auto label = label_[idx]; - PADDLE_ASSERT_MSG(label >= 0 && label < feature_size_, - "The label is out of the range.", label); if (label != ignore_index_) { + PADDLE_ASSERT_MSG(label >= 0 && label < feature_size_, + "The label is out of the range.", label); auto match_x = x_[idx * feature_size_ + label]; y_[idx] = -math::TolerableValue()(real_log(match_x)); match_x_[idx] = match_x; diff --git a/paddle/fluid/operators/cvm_op.h b/paddle/fluid/operators/cvm_op.h index 77cb7e446b7bc8179dc4832fa55cce4754e06ced..c6140483ff5cb8108895546b6a01f058708231fd 100644 --- a/paddle/fluid/operators/cvm_op.h +++ b/paddle/fluid/operators/cvm_op.h @@ -73,8 +73,8 @@ class CVMOpKernel : public framework::OpKernel { } } else { auto lod = x->lod()[0]; - for (int i = 0; i < lod.size() - 1; ++i) { - for (int j = 0; j < lod[i + 1] - lod[i]; ++j) { + for (size_t i = 0; i < lod.size() - 1; ++i) { + for (size_t j = 0; j < lod[i + 1] - lod[i]; ++j) { CvmComputeKernel(use_cvm, item_size, &x_data, &y_data); } } @@ -113,7 +113,7 @@ class CVMGradOpKernel : public framework::OpKernel { auto lod = dx->lod()[0]; int seq_num = static_cast(lod.size()) - 1; for (int i = 0; i < seq_num; ++i) { - for (int j = 0; j < lod[i + 1] - lod[i]; ++j) { + for (size_t j = 0; j < lod[i + 1] - lod[i]; ++j) { CvmGradComputeKernel(use_cvm, item_size, *cvm_data, &dout_data, &dx_data); } diff --git a/paddle/fluid/operators/detection/CMakeLists.txt b/paddle/fluid/operators/detection/CMakeLists.txt index 94a2016aa53212c3ae5af6d86cccb117855cc3b4..f1c504d6e4bd065e4221b1207a117ff0f6732459 100644 --- a/paddle/fluid/operators/detection/CMakeLists.txt +++ b/paddle/fluid/operators/detection/CMakeLists.txt @@ -35,13 +35,17 @@ detection_library(box_clip_op SRCS box_clip_op.cc box_clip_op.cu) detection_library(yolov3_loss_op SRCS yolov3_loss_op.cc) detection_library(yolo_box_op SRCS yolo_box_op.cc yolo_box_op.cu) detection_library(box_decoder_and_assign_op SRCS box_decoder_and_assign_op.cc box_decoder_and_assign_op.cu) +detection_library(sigmoid_focal_loss_op SRCS sigmoid_focal_loss_op.cc sigmoid_focal_loss_op.cu) +detection_library(retinanet_detection_output_op SRCS retinanet_detection_output_op.cc) if(WITH_GPU) detection_library(generate_proposals_op SRCS generate_proposals_op.cc generate_proposals_op.cu DEPS memory cub) detection_library(distribute_fpn_proposals_op SRCS distribute_fpn_proposals_op.cc distribute_fpn_proposals_op.cu DEPS memory cub) + detection_library(collect_fpn_proposals_op SRCS collect_fpn_proposals_op.cc collect_fpn_proposals_op.cu DEPS memory cub) else() detection_library(generate_proposals_op SRCS generate_proposals_op.cc) detection_library(distribute_fpn_proposals_op SRCS distribute_fpn_proposals_op.cc) + detection_library(collect_fpn_proposals_op SRCS collect_fpn_proposals_op.cc) endif() detection_library(roi_perspective_transform_op SRCS roi_perspective_transform_op.cc roi_perspective_transform_op.cu) diff --git a/paddle/fluid/operators/detection/bbox_util.h b/paddle/fluid/operators/detection/bbox_util.h index d4cf9a326cc5000e8e75322b59aefc3fb18e86b6..afc39c1db9fba8bf01a78ade83af1037a83d8d9d 100644 --- a/paddle/fluid/operators/detection/bbox_util.h +++ b/paddle/fluid/operators/detection/bbox_util.h @@ -22,10 +22,10 @@ namespace paddle { namespace operators { struct RangeInitFunctor { - int start_; - int delta_; - int* out_; - HOSTDEVICE void operator()(size_t i) { out_[i] = start_ + i * delta_; } + int start; + int delta; + int* out; + HOSTDEVICE void operator()(size_t i) { out[i] = start + i * delta; } }; template diff --git a/paddle/fluid/operators/detection/distribute_fpn_proposals_op.cu b/paddle/fluid/operators/detection/distribute_fpn_proposals_op.cu index 598510870a671468ba9b72438235f2dfec122401..f34866360f91b8e75d8e0e89425ba2b2e83af8af 100644 --- a/paddle/fluid/operators/detection/distribute_fpn_proposals_op.cu +++ b/paddle/fluid/operators/detection/distribute_fpn_proposals_op.cu @@ -140,8 +140,7 @@ class GPUDistributeFpnProposalsOpKernel : public framework::OpKernel { target_lvls_data, keys_out, idx_in, idx_out, roi_num); // Allocate temporary storage - auto d_temp_storage = memory::Alloc(place, temp_storage_bytes, - memory::Allocator::kScratchpad); + auto d_temp_storage = memory::Alloc(place, temp_storage_bytes); // Run sorting operation // sort target level to get corresponding index diff --git a/paddle/fluid/operators/detection/generate_mask_labels_op.cc b/paddle/fluid/operators/detection/generate_mask_labels_op.cc index 46727c29de13c1213694540e6614a05f9008d232..38eafa5fe8fc6fb1437caa98245d853e0e1566cb 100644 --- a/paddle/fluid/operators/detection/generate_mask_labels_op.cc +++ b/paddle/fluid/operators/detection/generate_mask_labels_op.cc @@ -323,6 +323,10 @@ class GenerateMaskLabelsKernel : public framework::OpKernel { auto gt_segms_lod = gt_segms->lod(); for (int i = 0; i < n; ++i) { + if (rois_lod[i] == rois_lod[i + 1]) { + lod0.emplace_back(num_mask); + continue; + } Tensor im_info_slice = im_info->Slice(i, i + 1); Tensor gt_classes_slice = gt_classes->Slice(gt_classes_lod[i], gt_classes_lod[i + 1]); diff --git a/paddle/fluid/operators/detection/generate_proposal_labels_op.cc b/paddle/fluid/operators/detection/generate_proposal_labels_op.cc index b9b8a5a53ae5b865d882407b4985a657cf85eccb..451e0ca85501bccd2588dd58d0c8efe7142559d9 100644 --- a/paddle/fluid/operators/detection/generate_proposal_labels_op.cc +++ b/paddle/fluid/operators/detection/generate_proposal_labels_op.cc @@ -109,17 +109,18 @@ std::vector> SampleFgBgGt( const platform::CPUDeviceContext& context, Tensor* iou, const Tensor& is_crowd, const int batch_size_per_im, const float fg_fraction, const float fg_thresh, const float bg_thresh_hi, - const float bg_thresh_lo, std::minstd_rand engine, const bool use_random) { + const float bg_thresh_lo, std::minstd_rand engine, const bool use_random, + const bool is_cascade_rcnn, const Tensor& rpn_rois) { std::vector fg_inds; std::vector bg_inds; - std::vector gt_inds; + std::vector mapped_gt_inds; int64_t gt_num = is_crowd.numel(); const int* crowd_data = is_crowd.data(); T* proposal_to_gt_overlaps = iou->data(); int64_t row = iou->dims()[0]; int64_t col = iou->dims()[1]; float epsilon = 0.00001; - + const T* rpn_rois_dt = rpn_rois.data(); // Follow the Faster RCNN's implementation for (int64_t i = 0; i < row; ++i) { const T* v = proposal_to_gt_overlaps + i * col; @@ -127,64 +128,82 @@ std::vector> SampleFgBgGt( if ((i < gt_num) && (crowd_data[i])) { max_overlap = -1.0; } - if (max_overlap > fg_thresh) { + if (is_cascade_rcnn && + ((rpn_rois_dt[i * 4 + 2] - rpn_rois_dt[i * 4 + 0] + 1) <= 0 || + (rpn_rois_dt[i * 4 + 3] - rpn_rois_dt[i * 4 + 1] + 1) <= 0)) { + continue; + } + if (max_overlap >= fg_thresh) { + // fg mapped gt label index for (int64_t j = 0; j < col; ++j) { T val = proposal_to_gt_overlaps[i * col + j]; auto diff = std::abs(max_overlap - val); if (diff < epsilon) { fg_inds.emplace_back(i); - gt_inds.emplace_back(j); + mapped_gt_inds.emplace_back(j); break; } } + } else if ((max_overlap >= bg_thresh_lo) && (max_overlap < bg_thresh_hi)) { + bg_inds.emplace_back(i); } else { - if ((max_overlap >= bg_thresh_lo) && (max_overlap < bg_thresh_hi)) { - bg_inds.emplace_back(i); - } + continue; } } - // Reservoir Sampling - std::uniform_real_distribution uniform(0, 1); - int fg_rois_per_im = std::floor(batch_size_per_im * fg_fraction); - int fg_rois_this_image = fg_inds.size(); - int fg_rois_per_this_image = std::min(fg_rois_per_im, fg_rois_this_image); - if (use_random) { - const int64_t fg_size = static_cast(fg_inds.size()); - if (fg_size > fg_rois_per_this_image) { - for (int64_t i = fg_rois_per_this_image; i < fg_size; ++i) { - int rng_ind = std::floor(uniform(engine) * i); - if (rng_ind < fg_rois_per_this_image) { - std::iter_swap(fg_inds.begin() + rng_ind, fg_inds.begin() + i); - std::iter_swap(gt_inds.begin() + rng_ind, gt_inds.begin() + i); + std::vector> res; + if (is_cascade_rcnn) { + res.emplace_back(fg_inds); + res.emplace_back(bg_inds); + res.emplace_back(mapped_gt_inds); + } else { + // Reservoir Sampling + // sampling fg + std::uniform_real_distribution uniform(0, 1); + int fg_rois_per_im = std::floor(batch_size_per_im * fg_fraction); + int fg_rois_this_image = fg_inds.size(); + int fg_rois_per_this_image = std::min(fg_rois_per_im, fg_rois_this_image); + if (use_random) { + const int64_t fg_size = static_cast(fg_inds.size()); + if (fg_size > fg_rois_per_this_image) { + for (int64_t i = fg_rois_per_this_image; i < fg_size; ++i) { + int rng_ind = std::floor(uniform(engine) * i); + if (rng_ind < fg_rois_per_this_image) { + std::iter_swap(fg_inds.begin() + rng_ind, fg_inds.begin() + i); + std::iter_swap(mapped_gt_inds.begin() + rng_ind, + mapped_gt_inds.begin() + i); + } } } } - } - std::vector new_fg_inds(fg_inds.begin(), - fg_inds.begin() + fg_rois_per_this_image); - std::vector new_gt_inds(gt_inds.begin(), - gt_inds.begin() + fg_rois_per_this_image); - - int bg_rois_per_image = batch_size_per_im - fg_rois_per_this_image; - int bg_rois_this_image = bg_inds.size(); - int bg_rois_per_this_image = std::min(bg_rois_per_image, bg_rois_this_image); - if (use_random) { - const int64_t bg_size = static_cast(bg_inds.size()); - if (bg_size > bg_rois_per_this_image) { - for (int64_t i = bg_rois_per_this_image; i < bg_size; ++i) { - int rng_ind = std::floor(uniform(engine) * i); - if (rng_ind < fg_rois_per_this_image) - std::iter_swap(bg_inds.begin() + rng_ind, bg_inds.begin() + i); + std::vector new_fg_inds(fg_inds.begin(), + fg_inds.begin() + fg_rois_per_this_image); + std::vector new_gt_inds( + mapped_gt_inds.begin(), + mapped_gt_inds.begin() + fg_rois_per_this_image); + // sampling bg + int bg_rois_per_image = batch_size_per_im - fg_rois_per_this_image; + int bg_rois_this_image = bg_inds.size(); + int bg_rois_per_this_image = + std::min(bg_rois_per_image, bg_rois_this_image); + if (use_random) { + const int64_t bg_size = static_cast(bg_inds.size()); + if (bg_size > bg_rois_per_this_image) { + for (int64_t i = bg_rois_per_this_image; i < bg_size; ++i) { + int rng_ind = std::floor(uniform(engine) * i); + if (rng_ind < fg_rois_per_this_image) + std::iter_swap(bg_inds.begin() + rng_ind, bg_inds.begin() + i); + } } } + std::vector new_bg_inds(bg_inds.begin(), + bg_inds.begin() + bg_rois_per_this_image); + // + res.emplace_back(new_fg_inds); + res.emplace_back(new_bg_inds); + res.emplace_back(new_gt_inds); } - std::vector new_bg_inds(bg_inds.begin(), - bg_inds.begin() + bg_rois_per_this_image); - std::vector> res; - res.emplace_back(new_fg_inds); - res.emplace_back(new_bg_inds); - res.emplace_back(new_gt_inds); + return res; } @@ -231,35 +250,50 @@ std::vector SampleRoisForOneImage( const Tensor& im_info, const int batch_size_per_im, const float fg_fraction, const float fg_thresh, const float bg_thresh_hi, const float bg_thresh_lo, const std::vector& bbox_reg_weights, const int class_nums, - std::minstd_rand engine, bool use_random) { + std::minstd_rand engine, bool use_random, bool is_cascade_rcnn, + bool is_cls_agnostic) { + // 1.1 map to original image auto im_scale = im_info.data()[2]; - + Tensor rpn_rois_slice; Tensor rpn_rois; - rpn_rois.mutable_data(rpn_rois_in.dims(), context.GetPlace()); - T* rpn_rois_dt = rpn_rois.data(); - const T* rpn_rois_in_dt = rpn_rois_in.data(); - for (int i = 0; i < rpn_rois.numel(); ++i) { - rpn_rois_dt[i] = rpn_rois_in_dt[i] / im_scale; + + if (is_cascade_rcnn) { + // slice rpn_rois from gt_box_num refer to detectron + rpn_rois_slice = + rpn_rois_in.Slice(gt_boxes.dims()[0], rpn_rois_in.dims()[0]); + rpn_rois.mutable_data(rpn_rois_slice.dims(), context.GetPlace()); + const T* rpn_rois_in_dt = rpn_rois_slice.data(); + T* rpn_rois_dt = rpn_rois.data(); + for (int i = 0; i < rpn_rois.numel(); ++i) { + rpn_rois_dt[i] = rpn_rois_in_dt[i] / im_scale; + } + } else { + rpn_rois.mutable_data(rpn_rois_in.dims(), context.GetPlace()); + const T* rpn_rois_in_dt = rpn_rois_in.data(); + T* rpn_rois_dt = rpn_rois.data(); + for (int i = 0; i < rpn_rois.numel(); ++i) { + rpn_rois_dt[i] = rpn_rois_in_dt[i] / im_scale; + } } - Tensor boxes; + // 1.2 compute overlaps int proposals_num = gt_boxes.dims()[0] + rpn_rois.dims()[0]; + Tensor boxes; boxes.mutable_data({proposals_num, kBoxDim}, context.GetPlace()); Concat(context, gt_boxes, rpn_rois, &boxes); - - // Overlaps Tensor proposal_to_gt_overlaps; proposal_to_gt_overlaps.mutable_data({proposals_num, gt_boxes.dims()[0]}, context.GetPlace()); BboxOverlaps(boxes, gt_boxes, &proposal_to_gt_overlaps); // Generate proposal index - std::vector> fg_bg_gt = SampleFgBgGt( - context, &proposal_to_gt_overlaps, is_crowd, batch_size_per_im, - fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, engine, use_random); + std::vector> fg_bg_gt = + SampleFgBgGt(context, &proposal_to_gt_overlaps, is_crowd, + batch_size_per_im, fg_fraction, fg_thresh, bg_thresh_hi, + bg_thresh_lo, engine, use_random, is_cascade_rcnn, boxes); std::vector fg_inds = fg_bg_gt[0]; std::vector bg_inds = fg_bg_gt[1]; - std::vector gt_inds = fg_bg_gt[2]; + std::vector mapped_gt_inds = fg_bg_gt[2]; // mapped_gt_labels // Gather boxes and labels Tensor sampled_boxes, sampled_labels, sampled_gts; @@ -271,7 +305,8 @@ std::vector SampleRoisForOneImage( sampled_labels.mutable_data({boxes_num}, context.GetPlace()); sampled_gts.mutable_data({fg_num, kBoxDim}, context.GetPlace()); GatherBoxesLabels(context, boxes, gt_boxes, gt_classes, fg_inds, bg_inds, - gt_inds, &sampled_boxes, &sampled_labels, &sampled_gts); + mapped_gt_inds, &sampled_boxes, &sampled_labels, + &sampled_gts); // Compute targets Tensor bbox_targets_single; @@ -305,6 +340,9 @@ std::vector SampleRoisForOneImage( for (int64_t i = 0; i < boxes_num; ++i) { int label = sampled_labels_data[i]; if (label > 0) { + if (is_cls_agnostic) { + label = 1; + } int dst_idx = i * width + kBoxDim * label; int src_idx = kBoxDim * i; bbox_targets_data[dst_idx] = bbox_targets_single_data[src_idx]; @@ -356,7 +394,8 @@ class GenerateProposalLabelsKernel : public framework::OpKernel { context.Attr>("bbox_reg_weights"); int class_nums = context.Attr("class_nums"); bool use_random = context.Attr("use_random"); - + bool is_cascade_rcnn = context.Attr("is_cascade_rcnn"); + bool is_cls_agnostic = context.Attr("is_cls_agnostic"); PADDLE_ENFORCE_EQ(rpn_rois->lod().size(), 1UL, "GenerateProposalLabelsOp rpn_rois needs 1 level of LoD"); PADDLE_ENFORCE_EQ( @@ -411,7 +450,7 @@ class GenerateProposalLabelsKernel : public framework::OpKernel { dev_ctx, rpn_rois_slice, gt_classes_slice, is_crowd_slice, gt_boxes_slice, im_info_slice, batch_size_per_im, fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, class_nums, - engine, use_random); + engine, use_random, is_cascade_rcnn, is_cls_agnostic); Tensor sampled_rois = tensor_output[0]; Tensor sampled_labels_int32 = tensor_output[1]; Tensor sampled_bbox_targets = tensor_output[2]; @@ -513,6 +552,13 @@ class GenerateProposalLabelsOpMaker : public framework::OpProtoAndCheckerMaker { "use_random", "Use random sampling to choose foreground and background boxes.") .SetDefault(true); + AddAttr("is_cascade_rcnn", + "cascade rcnn sampling policy changed from stage 2.") + .SetDefault(false); + AddAttr( + "is_cls_agnostic", + "the box regress will only include fg and bg locations if set true ") + .SetDefault(false); AddComment(R"DOC( This operator can be, for given the GenerateProposalOp output bounding boxes and groundtruth, diff --git a/paddle/fluid/operators/detection/generate_proposals_op.cu b/paddle/fluid/operators/detection/generate_proposals_op.cu index 2dfd9befdb7e536f388e439dc1449a709185509c..43deb5f9f3871b69ca46b7908c56c1236c1c5595 100644 --- a/paddle/fluid/operators/detection/generate_proposals_op.cu +++ b/paddle/fluid/operators/detection/generate_proposals_op.cu @@ -70,8 +70,7 @@ static void SortDescending(const platform::CUDADeviceContext &ctx, nullptr, temp_storage_bytes, keys_in, keys_out, idx_in, idx_out, num); // Allocate temporary storage auto place = boost::get(ctx.GetPlace()); - auto d_temp_storage = - memory::Alloc(place, temp_storage_bytes, memory::Allocator::kScratchpad); + auto d_temp_storage = memory::Alloc(place, temp_storage_bytes); // Run sorting operation cub::DeviceRadixSort::SortPairsDescending( diff --git a/paddle/fluid/operators/detection/rpn_target_assign_op.cc b/paddle/fluid/operators/detection/rpn_target_assign_op.cc index 0b8053e8d03c426e5a1b619e67bc8dae21c5c024..338954346c5af2c04ff6bf09b11873caec4a04dd 100644 --- a/paddle/fluid/operators/detection/rpn_target_assign_op.cc +++ b/paddle/fluid/operators/detection/rpn_target_assign_op.cc @@ -202,21 +202,32 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data, } // Reservoir Sampling - int fg_num = static_cast(rpn_fg_fraction * rpn_batch_size_per_im); - ReservoirSampling(fg_num, &fg_inds_fake, engine, use_random); + int fg_num = 0; + if (rpn_fg_fraction > 0 && rpn_batch_size_per_im > 0) { + fg_num = static_cast(rpn_fg_fraction * rpn_batch_size_per_im); + ReservoirSampling(fg_num, &fg_inds_fake, engine, use_random); + } else { + fg_num = static_cast(fg_inds_fake.size()); + } int fg_fake_num = static_cast(fg_inds_fake.size()); for (int64_t i = 0; i < fg_fake_num; ++i) { target_label[fg_inds_fake[i]] = 1; } - int bg_num = rpn_batch_size_per_im - fg_fake_num; for (int64_t i = 0; i < anchor_num; ++i) { if (anchor_to_gt_max_data[i] < rpn_negative_overlap) { bg_inds_fake.push_back(i); } } - ReservoirSampling(bg_num, &bg_inds_fake, engine, use_random); - bg_num = static_cast(bg_inds_fake.size()); + int bg_num = 0; + if (rpn_fg_fraction > 0 && rpn_batch_size_per_im > 0) { + bg_num = rpn_batch_size_per_im - fg_fake_num; + ReservoirSampling(bg_num, &bg_inds_fake, engine, use_random); + bg_num = static_cast(bg_inds_fake.size()); + } else { + bg_num = static_cast(bg_inds_fake.size()); + } + int fake_num = 0; for (int64_t i = 0; i < bg_num; ++i) { // fg fake found @@ -492,9 +503,9 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker { AddInput("Anchor", "(Tensor) input anchor is a 2-D Tensor with shape [H*W*A, 4]."); AddInput("GtBoxes", - "(LoDTensor) input groud-truth bbox with shape [K, 4]."); + "(LoDTensor) input ground-truth bbox with shape [K, 4]."); AddInput("IsCrowd", - "(LoDTensor) input which indicates groud-truth is crowd."); + "(LoDTensor) input which indicates ground-truth is crowd."); AddInput("ImInfo", "(LoDTensor) input image information with shape [N, 3]. " "N is the batch size, each image information includes height, " @@ -536,7 +547,7 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker { "ScoreIndex", "(Tensor), The indexes of foreground and background anchors in all " "RPN anchors(The rest anchors are ignored). The shape of the " - "ScoreIndex is [F + B], F and B are sampled foreground and backgroud " + "ScoreIndex is [F + B], F and B are sampled foreground and background " " number."); AddOutput("TargetBBox", "(Tensor), The target bbox deltas with shape " @@ -544,7 +555,7 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker { AddOutput( "TargetLabel", "(Tensor), The target labels of each anchor with shape " - "[F + B, 1], F and B are sampled foreground and backgroud number."); + "[F + B, 1], F and B are sampled foreground and background number."); AddOutput("BBoxInsideWeight", "(Tensor), The bbox inside weight with shape " "[F, 4], F is the sampled foreground number."); @@ -573,6 +584,440 @@ negative do not contribute to the training objective. } }; +class RetinanetTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker { + public: + void Make() override { + AddInput("Anchor", + "(Tensor) input anchor is a 2-D Tensor with shape [H*W*A, 4]."); + AddInput("GtBoxes", + "(LoDTensor) input ground-truth bbox with shape [K, 4]."); + AddInput("GtLabels", + "(LoDTensor) input ground-truth label with shape [K, 1]."); + AddInput("IsCrowd", + "(LoDTensor) input which indicates ground-truth is crowd."); + AddInput("ImInfo", + "(LoDTensor) input image information with shape [N, 3]. " + "N is the batch size, each image information includes height, " + "width and scale."); + AddAttr( + "positive_overlap", + "Minimum overlap required between an anchor and ground-truth " + "box for the (anchor, gt box) pair to be a positive example.") + .SetDefault(0.5); + AddAttr( + "negative_overlap", + "Maximum overlap allowed between an anchor and ground-truth " + "box for the (anchor, gt box) pair to be a negative examples.") + .SetDefault(0.4); + AddOutput( + "LocationIndex", + "(Tensor), The indexes of foreground anchors in all anchors, the " + "shape of the LocationIndex is [F], F depends on the value of input " + "tensor and attributes."); + AddOutput( + "ScoreIndex", + "(Tensor), The indexes of foreground and background anchors in all " + "RPN anchors(The rest anchors are ignored). The shape of the " + "ScoreIndex is [F + B], F and B are foreground and background " + " number."); + AddOutput("TargetBBox", + "(Tensor), The target bbox deltas with shape " + "[F, 4], F is the foreground number."); + AddOutput("TargetLabel", + "(Tensor), The target labels of each anchor with shape " + "[F + B, 1], F and B are foreground and background number."); + AddOutput("BBoxInsideWeight", + "(Tensor), The bbox inside weight with shape " + "[F, 4], F is the foreground number."); + AddOutput("ForegroundNumber", + "(Tensor), The foreground number. " + "[1, 1]."); + AddComment(R"DOC( + This layer can be, for given the Intersection-over-Union (IoU) overlap + between anchors and ground truth boxes, to assign classification and + regression targets to each anchor, these target labels are used for + train retinanet. + + Every anchor is assigned with a length C one-hot vector of + classification targets, and a 4-vector of box regression targets, + where C is the class number. The assignment rules are as followed: + + 1. Anchors are assigned to ground-truth boxes when: (i) it has the highest + IoU overlap with a ground-truth box, or (ii) it has an IoU overlap higher + than positive_overlap(0.5) with any ground-truth box. + + 2. Anchors are assigned to background when its IoU ratio is lower than + negative_overlap (0.4) for all ground-truth boxes. + + When an anchor is assigned with a ground-truth box which is the i-th category, + the i-th entry in its C vector of targets is set to 1 and all other entries + are set to 0. When an anchor is assigned with background, all entries are set + to 0. Anchors that are not assigned do not contribute to the training + objective. The regression targets are the encoded ground-truth boxes + associated with the assigned anchors. + +)DOC"); + } +}; + +class RetinanetTargetAssignOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE( + ctx->HasInput("Anchor"), + "Input(Anchor) of RetinanetTargetAssignOp should not be null"); + PADDLE_ENFORCE( + ctx->HasInput("GtBoxes"), + "Input(GtBoxes) of RetinanetTargetAssignOp should not be null"); + PADDLE_ENFORCE( + ctx->HasInput("GtLabels"), + "Input(GtLabels) of RetinanetTargetAssignOp should not be null"); + PADDLE_ENFORCE( + ctx->HasInput("IsCrowd"), + "Input(Anchor) of RetinanetTargetAssignOp should not be null"); + PADDLE_ENFORCE( + ctx->HasInput("ImInfo"), + "Input(ImInfo) of RetinanetTargetAssignOp should not be null"); + + PADDLE_ENFORCE( + ctx->HasOutput("LocationIndex"), + "Output(LocationIndex) of RetinanetTargetAssignOp should not be null"); + PADDLE_ENFORCE( + ctx->HasOutput("ScoreIndex"), + "Output(ScoreIndex) of RetinanetTargetAssignOp should not be null"); + PADDLE_ENFORCE( + ctx->HasOutput("TargetLabel"), + "Output(TargetLabel) of RetinanetTargetAssignOp should not be null"); + PADDLE_ENFORCE( + ctx->HasOutput("TargetBBox"), + "Output(TargetBBox) of RetinanetTargetAssignOp should not be null"); + PADDLE_ENFORCE(ctx->HasOutput("BBoxInsideWeight"), + "Output(BBoxInsideWeight) of RetinanetTargetAssignOp should " + "not be null"); + PADDLE_ENFORCE(ctx->HasOutput("ForegroundNumber"), + "Output(ForegroundNumber) of RetinanetTargetAssignOp should " + "not be null"); + + auto anchor_dims = ctx->GetInputDim("Anchor"); + auto gt_boxes_dims = ctx->GetInputDim("GtBoxes"); + auto gt_labels_dims = ctx->GetInputDim("GtLabels"); + auto im_info_dims = ctx->GetInputDim("ImInfo"); + + PADDLE_ENFORCE_EQ(anchor_dims.size(), 2, + "The rank of Input(Anchor) must be 2."); + PADDLE_ENFORCE_EQ(gt_boxes_dims.size(), 2, + "The rank of Input(GtBoxes) must be 2."); + PADDLE_ENFORCE_EQ(gt_labels_dims.size(), 2, + "The rank of Input(GtLabels) must be 2."); + PADDLE_ENFORCE_EQ(im_info_dims.size(), 2, + "The rank of Input(ImInfo) must be 2."); + + ctx->SetOutputDim("LocationIndex", {gt_labels_dims[0]}); + ctx->SetOutputDim("ScoreIndex", {gt_labels_dims[0]}); + ctx->SetOutputDim("TargetBBox", {gt_labels_dims[0], 4}); + ctx->SetOutputDim("TargetLabel", {gt_labels_dims[0], 1}); + ctx->SetOutputDim("BBoxInsideWeight", {gt_labels_dims[0], 4}); + ctx->SetOutputDim("ForegroundNumber", {gt_labels_dims[0], 1}); + } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + return framework::OpKernelType( + ctx.Input("Anchor")->type(), + platform::CPUPlace()); + } +}; + +template +std::vector FilterCrowdGtBoxLabel( + const platform::CPUDeviceContext& context, Tensor* gt_boxes, + Tensor* gt_labels, Tensor* is_crowd) { + int gt_num = gt_boxes->dims()[0]; + std::vector not_crowd_inds; + auto* is_crowd_data = is_crowd->data(); + for (int i = 0; i < gt_num; ++i) { + if (is_crowd_data[i] == 0) { + not_crowd_inds.emplace_back(i); + } + } + int ncrowd_num = not_crowd_inds.size(); + Tensor ncrowd_gt_boxes, ncrowd_gt_labels; + T* ncrowd_gt_boxes_data = + ncrowd_gt_boxes.mutable_data({ncrowd_num, 4}, context.GetPlace()); + int* ncrowd_gt_labels_data = + ncrowd_gt_labels.mutable_data({ncrowd_num, 1}, context.GetPlace()); + Gather(gt_boxes->data(), 4, not_crowd_inds.data(), ncrowd_num, + ncrowd_gt_boxes_data); + Gather(gt_labels->data(), 1, not_crowd_inds.data(), ncrowd_num, + ncrowd_gt_labels_data); + std::vector res; + res.emplace_back(ncrowd_gt_boxes); + res.emplace_back(ncrowd_gt_labels); + return res; +} + +template +std::vector GetAllFgBgGt(const platform::CPUDeviceContext& ctx, + const Tensor& anchor_by_gt_overlap, + const Tensor& ncrowd_gt_labels, + const float positive_overlap, + const float negative_overlap, + std::minstd_rand engine) { + auto* anchor_by_gt_overlap_data = anchor_by_gt_overlap.data(); + int anchor_num = anchor_by_gt_overlap.dims()[0]; + int gt_num = anchor_by_gt_overlap.dims()[1]; + + std::vector fg_inds; + std::vector bg_inds; + std::vector gt_inds; + std::vector tgt_lbl; + std::vector fg_fake; + std::vector bbox_inside_weight; + // Calculate the max IoU between anchors and gt boxes + // Map from anchor to gt box that has highest overlap + auto place = ctx.GetPlace(); + Tensor anchor_to_gt_max, anchor_to_gt_argmax, gt_to_anchor_max; + anchor_to_gt_max.mutable_data({anchor_num}, place); + int* argmax = anchor_to_gt_argmax.mutable_data({anchor_num}, place); + gt_to_anchor_max.mutable_data({gt_num}, place); + + auto anchor_by_gt_overlap_et = + framework::EigenMatrix::From(anchor_by_gt_overlap); + auto anchor_to_gt_max_et = + framework::EigenVector::Flatten(anchor_to_gt_max); + auto gt_to_anchor_max_et = + framework::EigenVector::Flatten(gt_to_anchor_max); + auto anchor_to_gt_argmax_et = + framework::EigenVector::Flatten(anchor_to_gt_argmax); + anchor_to_gt_max_et = + anchor_by_gt_overlap_et.maximum(Eigen::DSizes(1)); + anchor_to_gt_argmax_et = + anchor_by_gt_overlap_et.argmax(1).template cast(); + gt_to_anchor_max_et = + anchor_by_gt_overlap_et.maximum(Eigen::DSizes(0)); + + ScoreAssign(anchor_by_gt_overlap_data, anchor_to_gt_max, gt_to_anchor_max, -1, + -1, positive_overlap, negative_overlap, &fg_inds, &bg_inds, + &tgt_lbl, &fg_fake, &bbox_inside_weight, engine, false); + const int* gt_labels_data = ncrowd_gt_labels.data(); + int64_t fg_num = fg_inds.size(); + for (int64_t i = 0; i < fg_num; ++i) { + int gt_idx = argmax[fg_inds[i]]; + tgt_lbl[i] = gt_labels_data[gt_idx]; + } + + int bg_num = bg_inds.size(); + int fg_fake_num = fg_fake.size(); + gt_inds.reserve(fg_fake_num); + for (int i = 0; i < fg_fake_num; ++i) { + gt_inds.emplace_back(argmax[fg_fake[i]]); + } + + Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t, bbox_inside_weight_t; + Tensor fg_num_t; + int* loc_index_data = loc_index_t.mutable_data({fg_fake_num}, place); + int* score_index_data = + score_index_t.mutable_data({fg_num + bg_num}, place); + int* tgt_lbl_data = tgt_lbl_t.mutable_data({fg_num + bg_num}, place); + int* gt_inds_data = gt_inds_t.mutable_data({fg_fake_num}, place); + int* fg_num_data = fg_num_t.mutable_data({1}, place); + T* bbox_inside_weight_data = + bbox_inside_weight_t.mutable_data({fg_fake_num, 4}, place); + std::copy(fg_fake.begin(), fg_fake.end(), loc_index_data); + std::copy(fg_inds.begin(), fg_inds.end(), score_index_data); + std::copy(bg_inds.begin(), bg_inds.end(), score_index_data + fg_num); + std::copy(tgt_lbl.begin(), tgt_lbl.end(), tgt_lbl_data); + std::copy(gt_inds.begin(), gt_inds.end(), gt_inds_data); + std::copy(bbox_inside_weight.begin(), bbox_inside_weight.end(), + bbox_inside_weight_data); + fg_num_data[0] = fg_fake.size() + 1; + std::vector loc_score_tgtlbl_gt; + loc_score_tgtlbl_gt.emplace_back(loc_index_t); + loc_score_tgtlbl_gt.emplace_back(score_index_t); + loc_score_tgtlbl_gt.emplace_back(tgt_lbl_t); + loc_score_tgtlbl_gt.emplace_back(gt_inds_t); + loc_score_tgtlbl_gt.emplace_back(bbox_inside_weight_t); + loc_score_tgtlbl_gt.emplace_back(fg_num_t); + + return loc_score_tgtlbl_gt; +} + +template +class RetinanetTargetAssignKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* anchor = context.Input("Anchor"); // (H*W*A) * 4 + auto* gt_boxes = context.Input("GtBoxes"); + auto* gt_labels = context.Input("GtLabels"); + auto* is_crowd = context.Input("IsCrowd"); + auto* im_info = context.Input("ImInfo"); + + auto* loc_index = context.Output("LocationIndex"); + auto* score_index = context.Output("ScoreIndex"); + auto* tgt_bbox = context.Output("TargetBBox"); + auto* tgt_lbl = context.Output("TargetLabel"); + auto* bbox_inside_weight = context.Output("BBoxInsideWeight"); + auto* fg_num = context.Output("ForegroundNumber"); + + PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL, + "RetinanetTargetAssignOp gt_boxes needs 1 level of LoD"); + PADDLE_ENFORCE_EQ(gt_labels->lod().size(), 1UL, + "RetinanetTargetAssignOp gt_boxes needs 1 level of LoD"); + PADDLE_ENFORCE_EQ(is_crowd->lod().size(), 1UL, + "RetinanetTargetAssignOp is_crowd needs 1 level of LoD"); + + int64_t anchor_num = static_cast(anchor->dims()[0]); + int64_t batch_num = static_cast(gt_boxes->lod().back().size() - 1); + + float positive_overlap = context.Attr("positive_overlap"); + float negative_overlap = context.Attr("negative_overlap"); + + int64_t max_num = batch_num * anchor_num; + auto place = context.GetPlace(); + + loc_index->mutable_data({max_num}, place); + score_index->mutable_data({max_num}, place); + tgt_bbox->mutable_data({max_num, 4}, place); + tgt_lbl->mutable_data({max_num, 1}, place); + bbox_inside_weight->mutable_data({max_num, 4}, place); + fg_num->mutable_data({batch_num, 1}, place); + auto& dev_ctx = context.device_context(); + + std::random_device rnd; + std::minstd_rand engine; + int seed = rnd(); + engine.seed(seed); + + framework::LoD lod_loc, loc_score, lod_fg; + std::vector lod0_loc(1, 0); + std::vector lod0_score(1, 0); + std::vector lod0_fg(1, 0); + + int total_loc_num = 0; + int total_score_num = 0; + int total_fg_num = 0; + auto gt_boxes_lod = gt_boxes->lod().back(); + auto gt_labels_lod = gt_labels->lod().back(); + auto is_crowd_lod = is_crowd->lod().back(); + for (int i = 0; i < batch_num; ++i) { + Tensor gt_boxes_slice = + gt_boxes->Slice(gt_boxes_lod[i], gt_boxes_lod[i + 1]); + Tensor gt_labels_slice = + gt_labels->Slice(gt_labels_lod[i], gt_labels_lod[i + 1]); + Tensor is_crowd_slice = + is_crowd->Slice(is_crowd_lod[i], is_crowd_lod[i + 1]); + Tensor im_info_slice = im_info->Slice(i, i + 1); + auto* im_info_data = im_info_slice.data(); + auto im_height = im_info_data[0]; + auto im_width = im_info_data[1]; + auto im_scale = im_info_data[2]; + + // Filter straddle anchor + std::vector filter_output = + FilterStraddleAnchor(dev_ctx, anchor, -1, im_height, im_width); + Tensor inds_inside = filter_output[0]; + Tensor inside_anchor = filter_output[1]; + + // Filter crowd gt + std::vector ncrowd_output = FilterCrowdGtBoxLabel( + dev_ctx, >_boxes_slice, >_labels_slice, &is_crowd_slice); + Tensor ncrowd_gt_boxes = ncrowd_output[0]; + Tensor ncrowd_gt_labels = ncrowd_output[1]; + + auto ncrowd_gt_boxes_et = + framework::EigenTensor::From(ncrowd_gt_boxes); + ncrowd_gt_boxes_et = ncrowd_gt_boxes_et * im_scale; + + Tensor anchor_by_gt_overlap; + anchor_by_gt_overlap.mutable_data( + {inside_anchor.dims()[0], ncrowd_gt_boxes.dims()[0]}, place); + BboxOverlaps(inside_anchor, ncrowd_gt_boxes, &anchor_by_gt_overlap); + + auto loc_score_tgtlbl_gt = + GetAllFgBgGt(dev_ctx, anchor_by_gt_overlap, ncrowd_gt_labels, + positive_overlap, negative_overlap, engine); + + Tensor sampled_loc_index = loc_score_tgtlbl_gt[0]; + Tensor sampled_score_index = loc_score_tgtlbl_gt[1]; + Tensor sampled_tgtlbl = loc_score_tgtlbl_gt[2]; + Tensor sampled_gt_index = loc_score_tgtlbl_gt[3]; + Tensor sampled_bbox_inside_weight = loc_score_tgtlbl_gt[4]; + Tensor sampled_fg_num = loc_score_tgtlbl_gt[5]; + + int loc_num = sampled_loc_index.dims()[0]; + int score_num = sampled_score_index.dims()[0]; + // unmap to all anchor + Tensor sampled_loc_index_unmap, sampled_score_index_unmap; + sampled_loc_index_unmap.mutable_data({loc_num}, place); + sampled_score_index_unmap.mutable_data({score_num}, place); + Gather(inds_inside.data(), 1, sampled_loc_index.data(), + loc_num, sampled_loc_index_unmap.data()); + Gather(inds_inside.data(), 1, sampled_score_index.data(), + score_num, sampled_score_index_unmap.data()); + + // get target bbox deltas + Tensor sampled_anchor, sampled_gt, sampled_tgt_bbox; + auto* sampled_anchor_data = + sampled_anchor.mutable_data({loc_num, 4}, place); + auto* sampled_gt_data = sampled_gt.mutable_data({loc_num, 4}, place); + Gather(anchor->data(), 4, sampled_loc_index_unmap.data(), + loc_num, sampled_anchor_data); + Gather(ncrowd_gt_boxes.data(), 4, sampled_gt_index.data(), + loc_num, sampled_gt_data); + sampled_tgt_bbox.mutable_data({loc_num, 4}, place); + BoxToDelta(loc_num, sampled_anchor, sampled_gt, nullptr, false, + &sampled_tgt_bbox); + + // Add anchor offset + int anchor_offset = i * anchor_num; + auto sampled_loc_index_unmap_et = + framework::EigenTensor::From(sampled_loc_index_unmap); + sampled_loc_index_unmap_et = sampled_loc_index_unmap_et + anchor_offset; + auto sampled_score_index_unmap_et = + framework::EigenTensor::From(sampled_score_index_unmap); + sampled_score_index_unmap_et = + sampled_score_index_unmap_et + anchor_offset; + AppendRpns(loc_index, total_loc_num, &sampled_loc_index_unmap); + AppendRpns(score_index, total_score_num, &sampled_score_index_unmap); + AppendRpns(tgt_bbox, total_loc_num * 4, &sampled_tgt_bbox); + AppendRpns(tgt_lbl, total_score_num, &sampled_tgtlbl); + AppendRpns(bbox_inside_weight, total_loc_num * 4, + &sampled_bbox_inside_weight); + AppendRpns(fg_num, total_fg_num, &sampled_fg_num); + + total_loc_num += loc_num; + total_score_num += score_num; + total_fg_num += 1; + lod0_loc.emplace_back(total_loc_num); + lod0_score.emplace_back(total_score_num); + lod0_fg.emplace_back(total_fg_num); + } + + PADDLE_ENFORCE_LE(total_loc_num, max_num); + PADDLE_ENFORCE_LE(total_score_num, max_num); + PADDLE_ENFORCE_LE(total_fg_num, batch_num); + + lod_loc.emplace_back(lod0_loc); + loc_score.emplace_back(lod0_score); + lod_fg.emplace_back(lod0_fg); + loc_index->set_lod(lod_loc); + score_index->set_lod(loc_score); + tgt_bbox->set_lod(lod_loc); + tgt_lbl->set_lod(loc_score); + bbox_inside_weight->set_lod(lod_loc); + fg_num->set_lod(lod_fg); + loc_index->Resize({total_loc_num}); + score_index->Resize({total_score_num}); + tgt_bbox->Resize({total_loc_num, 4}); + tgt_lbl->Resize({total_score_num, 1}); + bbox_inside_weight->Resize({total_loc_num, 4}); + fg_num->Resize({total_fg_num, 1}); + } +}; + } // namespace operators } // namespace paddle @@ -582,3 +1027,9 @@ REGISTER_OPERATOR(rpn_target_assign, ops::RpnTargetAssignOp, paddle::framework::EmptyGradOpMaker); REGISTER_OP_CPU_KERNEL(rpn_target_assign, ops::RpnTargetAssignKernel, ops::RpnTargetAssignKernel); +REGISTER_OPERATOR(retinanet_target_assign, ops::RetinanetTargetAssignOp, + ops::RetinanetTargetAssignOpMaker, + paddle::framework::EmptyGradOpMaker); +REGISTER_OP_CPU_KERNEL(retinanet_target_assign, + ops::RetinanetTargetAssignKernel, + ops::RetinanetTargetAssignKernel); diff --git a/paddle/fluid/operators/distributed/CMakeLists.txt b/paddle/fluid/operators/distributed/CMakeLists.txt index f6531ec9edca7b425d28853f542d5e46783ba699..8909135d234a67a6a8d1fbc21eb0b04e67f8d17b 100644 --- a/paddle/fluid/operators/distributed/CMakeLists.txt +++ b/paddle/fluid/operators/distributed/CMakeLists.txt @@ -29,7 +29,7 @@ if(WITH_GRPC) set(RPC_DEPS sendrecvop_rpc ${GRPC_DEPS}) cc_test(grpc_serde_test SRCS grpc/grpc_serde_test.cc - DEPS ${RPC_DEPS} scope profiler math_function SERIAL) + DEPS ${RPC_DEPS} scope profiler math_function) else() set(BRPC_SRCS brpc/brpc_client.cc brpc/brpc_server.cc brpc/brpc_sendrecvop_utils.cc brpc/brpc_variable_response.cc brpc/brpc_rdma_pool.cc) @@ -47,12 +47,12 @@ else() set(RPC_DEPS sendrecvop_rpc ${BRPC_DEPS}) cc_test(brpc_serde_test SRCS brpc/brpc_serde_test.cc - DEPS ${RPC_DEPS} gflags glog executor proto_desc lookup_sparse_table_op SERIAL) + DEPS ${RPC_DEPS} gflags glog executor proto_desc lookup_sparse_table_op) endif() cc_test(rpc_server_test SRCS rpc_server_test.cc - DEPS ${RPC_DEPS} executor proto_desc lookup_sparse_table_op SERIAL) + DEPS ${RPC_DEPS} executor proto_desc lookup_sparse_table_op) cc_test(varhandle_test SRCS varhandle_test.cc DEPS profiler scope) cc_library(parameter_prefetch SRCS parameter_prefetch.cc DEPS sendrecvop_rpc memory) cc_library(parameter_send SRCS parameter_send.cc DEPS sendrecvop_rpc memory) @@ -62,5 +62,5 @@ cc_test(communicator_test SRCS communicator_test.cc DEPS communicator) if(WITH_GPU) cc_test(collective_server_test SRCS collective_server_test.cc DEPS sendrecvop_rpc executor ${RPC_DEPS} - selected_rows_functor scope math_function SERIAL) + selected_rows_functor scope math_function) endif() diff --git a/paddle/fluid/operators/distributed/communicator.cc b/paddle/fluid/operators/distributed/communicator.cc index b528bcdd32b11d686f44596d9a1bb663b21691f4..3a185667e7a70d315bc14ca018f181c3de6ca421 100644 --- a/paddle/fluid/operators/distributed/communicator.cc +++ b/paddle/fluid/operators/distributed/communicator.cc @@ -15,6 +15,7 @@ limitations under the License. */ #include "paddle/fluid/operators/distributed/communicator.h" #include +#include #include // NOLINT #include // NOLINT @@ -29,7 +30,7 @@ DEFINE_bool(communicator_independent_recv_thread, true, "use an independent to recv vars from parameter server"); DEFINE_int32(communicator_send_queue_size, 20, "queue size to recv gradient before send"); -DEFINE_int32(communicator_max_send_grad_num_before_recv, 20, +DEFINE_int32(communicator_min_send_grad_num_before_recv, 20, "max grad num to send before recv parameters"); DEFINE_int32(communicator_thread_pool_size, 5, "thread num to do send or recv"); DEFINE_int32(communicator_send_wait_times, 5, @@ -50,8 +51,7 @@ inline double GetCurrentUS() { return 1e+6 * time.tv_sec + time.tv_usec; } -std::unique_ptr Communicator::communicator_(nullptr); -std::once_flag Communicator::init_flag_; +std::shared_ptr Communicator::communicator_(nullptr); Communicator::Communicator(const RpcCtxMap &send_varname_to_ctx, const RpcCtxMap &recv_varname_to_ctx, @@ -64,8 +64,8 @@ Communicator::Communicator(const RpcCtxMap &send_varname_to_ctx, << FLAGS_communicator_independent_recv_thread; VLOG(0) << "communicator_send_queue_size: " << FLAGS_communicator_send_queue_size; - VLOG(0) << "communicator_max_send_grad_num_before_recv: " - << FLAGS_communicator_max_send_grad_num_before_recv; + VLOG(0) << "communicator_min_send_grad_num_before_recv: " + << FLAGS_communicator_min_send_grad_num_before_recv; VLOG(0) << "communicator_thread_pool_size: " << FLAGS_communicator_thread_pool_size; VLOG(0) << "communicator_send_wait_times: " @@ -84,11 +84,17 @@ Communicator::Communicator(const RpcCtxMap &send_varname_to_ctx, } Communicator::~Communicator() { - VLOG(3) << "~Communicator"; + if (FLAGS_v >= 3) { + std::string msg("~Communicator"); + fwrite(msg.c_str(), msg.length(), 1, stdout); + } running_ = false; if (send_thread_) send_thread_->join(); if (recv_thread_) recv_thread_->join(); - VLOG(3) << "~Communicator done"; + if (FLAGS_v >= 3) { + std::string msg("~Communicator done"); + fwrite(msg.c_str(), msg.length(), 1, stdout); + } } void Communicator::SendThread() { @@ -144,7 +150,7 @@ void Communicator::SendThread() { task_futures.emplace_back( send_threadpool_->enqueue(std::move(send_task))); } else { - VLOG(3) << var_name << " queue empty"; + VLOG(4) << var_name << " queue empty"; } } for (auto &task_f : task_futures) { @@ -160,17 +166,19 @@ void Communicator::SendThread() { RecvAll(); } } + VLOG(0) << "communicator stopped, send thread exit"; } void Communicator::RecvAll() { VLOG(3) << "parallel run recv graph"; + if (!running_) return; auto before_send = GetCurrentUS(); std::vector> task_futures; task_futures.reserve(recv_varname_to_ctx_.size()); for (auto &iter : recv_varname_to_ctx_) { auto recv_task = [this, &iter] { auto &var_name = iter.first; - VLOG(3) << "recv var " << var_name; + VLOG(4) << "recv var " << var_name; auto recv_functor = distributed::ParameterRecv(); if (!FLAGS_communicator_fake_rpc) { recv_functor(iter.second, *recv_scope_); @@ -189,7 +197,7 @@ void Communicator::RecvThread() { VLOG(3) << "RecvThread start!"; while (running_) { auto grad_num = grad_num_.load(); - if (grad_num > FLAGS_communicator_max_send_grad_num_before_recv) { + if (grad_num > FLAGS_communicator_min_send_grad_num_before_recv) { VLOG(1) << "current grad num " << grad_num; RecvAll(); grad_num_.store(0); @@ -197,6 +205,7 @@ void Communicator::RecvThread() { std::this_thread::sleep_for(std::chrono::milliseconds(10)); } } + VLOG(0) << "communicator stopped, recv thread exit"; } void Communicator::Send(const std::string &var_name, @@ -212,17 +221,90 @@ void Communicator::Send(const std::string &var_name, queue->Push(tmp_grad_var); } +void Communicator::Init(const paddle::framework::ProgramDesc &program, + Scope *param_scope) { + using RpcCtxMap = operators::distributed::RpcCtxMap; + VLOG(3) << "ProcessGraph"; + RpcCtxMap send_varname_to_ctx; + RpcCtxMap recv_varname_to_ctx; + for (auto *op : program.Block(0).AllOps()) { + VLOG(3) << "node name " << op->Type(); + if (op->Type() == "send") { + auto send_var_name = op->Input("X")[0]; + auto send_varnames = boost::get>( + op->GetNullableAttr("send_varnames")); + auto epmap = + boost::get>(op->GetNullableAttr("epmap")); + auto height_section = + boost::get>(op->GetNullableAttr("sections")); + auto trainer_id = boost::get(op->GetNullableAttr("trainer_id")); + send_varname_to_ctx[send_var_name] = operators::distributed::RpcContext( + send_var_name, send_varnames, epmap, height_section, trainer_id); + VLOG(3) << "find and init an send op: " + << send_varname_to_ctx[send_var_name]; + } else if (op->Type() == "recv") { + auto do_not_run = boost::get(op->GetNullableAttr("do_not_run")); + PADDLE_ENFORCE_GT(do_not_run, 0, "recv should not run!"); + auto recv_var_name = op->Output("Out")[0]; + auto recv_varnames = boost::get>( + op->GetNullableAttr("recv_varnames")); + auto epmap = + boost::get>(op->GetNullableAttr("epmap")); + auto trainer_id = boost::get(op->GetNullableAttr("trainer_id")); + recv_varname_to_ctx[recv_var_name] = operators::distributed::RpcContext( + recv_var_name, recv_varnames, epmap, {}, trainer_id); + } + } + + // init communicator here + if (send_varname_to_ctx.size() == 0 && recv_varname_to_ctx.size() == 0) { + LOG(WARNING) << "no var need to send and recv!!"; + } + operators::distributed::Communicator::Init(send_varname_to_ctx, + recv_varname_to_ctx, param_scope); +} + Communicator *Communicator::GetInstance() { return communicator_.get(); } +std::shared_ptr Communicator::GetInstantcePtr() { + return communicator_; +} + void Communicator::Start() { - running_ = true; - // start send and recv thread - send_thread_.reset( - new std::thread(std::bind(&Communicator::SendThread, this))); - if (FLAGS_communicator_independent_recv_thread) { - recv_thread_.reset( - new std::thread(std::bind(&Communicator::RecvThread, this))); + VLOG(0) << "Communicator start"; + if (!communicator_) { + VLOG(0) << "Communicator is not inited, do nothing"; + } else { + VLOG(1) << "start send thread and recv thread"; + running_ = true; + // start send and recv thread + send_thread_.reset( + new std::thread(std::bind(&Communicator::SendThread, this))); + if (FLAGS_communicator_independent_recv_thread) { + recv_thread_.reset( + new std::thread(std::bind(&Communicator::RecvThread, this))); + } + } +} + +void Communicator::Stop() { + VLOG(0) << "Communicator stop"; + running_ = false; + if (!communicator_) { + VLOG(0) << "Communicator is not inited, do nothing"; + } else { + if (send_thread_) { + VLOG(1) << "stop send thread"; + send_thread_->join(); + send_thread_.reset(nullptr); + } + if (recv_thread_) { + VLOG(1) << "stop recv thread"; + recv_thread_->join(); + recv_thread_.reset(nullptr); + } } + VLOG(0) << "Communicator stop done"; } } // namespace distributed diff --git a/paddle/fluid/operators/distributed/communicator.h b/paddle/fluid/operators/distributed/communicator.h index 37c39eb15112f745f6a25e95ce65d431d825182e..17f68fb4f1b86b22e9d422e4c0421a2bd2515586 100644 --- a/paddle/fluid/operators/distributed/communicator.h +++ b/paddle/fluid/operators/distributed/communicator.h @@ -165,6 +165,7 @@ class Communicator { ~Communicator(); void Start(); + void Stop(); // send grad void Send(const std::string& var_name, const framework::Scope& scope); @@ -181,8 +182,8 @@ class Communicator { send_varname_to_queue_; RpcCtxMap send_varname_to_ctx_; RpcCtxMap recv_varname_to_ctx_; - std::unique_ptr send_thread_; - std::unique_ptr recv_thread_; + std::unique_ptr send_thread_{nullptr}; + std::unique_ptr recv_thread_{nullptr}; Scope* recv_scope_; // should be global scope std::unique_ptr send_scope_; // an independent scope std::unique_ptr<::ThreadPool> send_threadpool_{nullptr}; @@ -193,25 +194,21 @@ class Communicator { public: static void Init(const RpcCtxMap& send_varname_to_ctx, const RpcCtxMap& recv_varname_to_ctx, Scope* recv_scope) { - InitImpl(send_varname_to_ctx, recv_varname_to_ctx, recv_scope); - } - - static Communicator* GetInstance(); - - private: - // Init is called by GetInstance. - static void InitImpl(const RpcCtxMap& send_varname_to_ctx, - const RpcCtxMap& recv_varname_to_ctx, - Scope* recv_scope) { if (communicator_ == nullptr) { communicator_.reset(new Communicator(send_varname_to_ctx, recv_varname_to_ctx, recv_scope)); } } + static void Init(const paddle::framework::ProgramDesc& program, + Scope* param_scope); + + static Communicator* GetInstance(); + + static std::shared_ptr GetInstantcePtr(); + private: - static std::once_flag init_flag_; - static std::unique_ptr communicator_; + static std::shared_ptr communicator_; }; } // namespace distributed diff --git a/paddle/fluid/operators/distributed/request_handler_impl.cc b/paddle/fluid/operators/distributed/request_handler_impl.cc index a41536368abc925531d1a54615546a100482a7eb..876b764a751f6a4aa73ec3aac0f23412cc8903c1 100644 --- a/paddle/fluid/operators/distributed/request_handler_impl.cc +++ b/paddle/fluid/operators/distributed/request_handler_impl.cc @@ -104,7 +104,7 @@ bool RequestGetHandler::Handle(const std::string& varname, } else { if (varname != FETCH_BARRIER_MESSAGE && varname != COMPLETE_MESSAGE) { if (enable_dc_asgd_) { - // NOTE: the format is determined by distributed_transpiler.py + // NOTE: the format is determined by distribute_transpiler.py std::string param_bak_name = string::Sprintf("%s.trainer_%d_bak", varname, trainer_id); VLOG(3) << "getting " << param_bak_name << " trainer_id " << trainer_id; diff --git a/paddle/fluid/operators/distributed/sendrecvop_utils.cc b/paddle/fluid/operators/distributed/sendrecvop_utils.cc index e5c96507e97267c3d0519a27a36cbac0336c7f28..9bd2c9928ccdb6416976b76e776fb22b28ea1f5d 100644 --- a/paddle/fluid/operators/distributed/sendrecvop_utils.cc +++ b/paddle/fluid/operators/distributed/sendrecvop_utils.cc @@ -15,6 +15,7 @@ limitations under the License. */ #ifdef PADDLE_WITH_CUDA #include #endif +#include #include // NOLINT #include "paddle/fluid/framework/data_type.h" @@ -39,8 +40,7 @@ static TensorPayload GetCommunicationAllocationFromTensor( reinterpret_cast(ctx); auto copy_size = tensor.numel() * framework::SizeOfType(tensor.type()); platform::CUDAPinnedPlace cuda_pinned; - auto result = memory::AllocShared( - cuda_pinned, copy_size, memory::allocation::Allocator::kCrossDevice); + auto result = memory::AllocShared(cuda_pinned, copy_size); memory::Copy(cuda_pinned, result->ptr(), boost::get(tensor.place()), diff --git a/paddle/fluid/operators/distributed_ops/allreduce_op.h b/paddle/fluid/operators/distributed_ops/allreduce_op.h index 8c143867618577740a29f971ac558c50113dff85..0275f6a9cf3aa8bab89b3d8c599b304702f590a8 100644 --- a/paddle/fluid/operators/distributed_ops/allreduce_op.h +++ b/paddle/fluid/operators/distributed_ops/allreduce_op.h @@ -39,6 +39,7 @@ class AllReduceOpKernel : public framework::OpKernel { auto& dev_ctx = ctx.template device_context(); auto in = ctx.Input("X"); auto out = ctx.Output("Out"); + int dtype = platform::ToNCCLDataType(in->type()); int64_t numel = in->numel(); auto* sendbuff = in->data(); @@ -66,12 +67,10 @@ class AllReduceOpKernel : public framework::OpKernel { red_type = ncclMin; break; } - VLOG(0) << "call allreduce with type: " << reduce_type; PADDLE_ENFORCE(platform::dynload::ncclAllReduce( sendbuff, recvbuff, numel, static_cast(dtype), red_type, comm, stream)); if (ctx.Attr("sync_mode")) { - VLOG(0) << "sync allreduce..."; cudaError_t e_sync = cudaStreamSynchronize(stream); if (e_sync != 0) { LOG(FATAL) << "cudaStreamSynchronize " << cudaGetErrorString(e_sync); diff --git a/paddle/fluid/operators/distributed_ops/gen_nccl_id_op.cc b/paddle/fluid/operators/distributed_ops/gen_nccl_id_op.cc index 80d712a0e02751485c78887782cf3dce76846cc1..c33842c06e49267e014c2927f6a7070cbe9a27ec 100644 --- a/paddle/fluid/operators/distributed_ops/gen_nccl_id_op.cc +++ b/paddle/fluid/operators/distributed_ops/gen_nccl_id_op.cc @@ -41,31 +41,132 @@ class GenNCCLIdOp : public framework::OperatorBase { // put nccl id in CPUPlace auto& dev_ctx = *pool.Get(platform::CPUPlace()); int trainer_id = Attr("trainer_id"); + + std::vector trainers = + Attr>("trainers"); + PADDLE_ENFORCE( + trainer_id >= 0 && trainer_id < static_cast(trainers.size()), + "trainer_id:%d must be in trainers.size range", trainer_id); + std::string endpoint = trainers[trainer_id]; + framework::Scope& local_scope = scope.NewScope(); + int nccl_comm_num = Attr("nccl_comm_num"); + int use_hierarchical_allreduce = Attr("use_hierarchical_allreduce"); + int inter_nranks = Attr("hierarchical_allreduce_inter_nranks"); + + int inter_trainer_id = -1; + int exter_trainer_id = -1; + if (use_hierarchical_allreduce) { + PADDLE_ENFORCE(trainers.size() > 1, "trainers.size():%llu < 1", + trainers.size()); + PADDLE_ENFORCE(inter_nranks > 1, "inter_nranks:%d < 1", inter_nranks); + PADDLE_ENFORCE((trainers.size() % inter_nranks == 0), + "trainers.size():%llu mod inter_nranks:%d != 0", + trainers.size(), inter_nranks); + + inter_trainer_id = trainer_id % inter_nranks; + + if (trainer_id % inter_nranks == 0) { + exter_trainer_id = trainer_id / inter_nranks; + } + } + + if (trainer_id != 0) { + GetIdByServer(endpoint, &local_scope, dev_ctx, nccl_comm_num, + use_hierarchical_allreduce, trainer_id, inter_trainer_id, + exter_trainer_id); + } + + std::ostringstream ss; + for (size_t i = 0; i < trainers.size(); i++) { + ss << trainers[i] << ","; + } + + VLOG(1) << "trainer_id:" << trainer_id + << ", use_hierarchical_allreduce:" << use_hierarchical_allreduce + << ", inter_nranks:" << inter_nranks + << ", inter_trainer_id:" << inter_trainer_id + << ", exter_trainer_id:" << exter_trainer_id + << ", trainers:" << ss.str(); + + // init flat if (trainer_id == 0) { - GenerateAndSend(&local_scope, dev_ctx); - } else { - GetIdByServer(&local_scope, dev_ctx); + std::vector flat_endpoints; + flat_endpoints.insert(flat_endpoints.begin(), trainers.begin() + 1, + trainers.end()); + // flat nccl_id + for (int i = 0; i < nccl_comm_num; i++) { + std::string var_name = platform::GetFlatNCCLVarName(i); + GenerateAndSend(&local_scope, dev_ctx, var_name, flat_endpoints); + } + } + + if (!use_hierarchical_allreduce) { + return; + } + + PADDLE_ENFORCE(trainers.size() % inter_nranks == 0, + "enpoints.size:%llu mod inter_nranks:%d should ==0", + trainers.size(), inter_nranks); + PADDLE_ENFORCE(inter_nranks > 1, "inter_nranks:%d must > 1", inter_nranks); + + // hierarchical inter ncclid + if (inter_trainer_id == 0) { + std::ostringstream ss; + ss << endpoint; + std::vector inter_endpoints; + for (int i = trainer_id + 1; i < trainer_id + inter_nranks && + i < static_cast(trainers.size()); + i++) { + ss << ","; + inter_endpoints.push_back(trainers[i]); + ss << trainers[i]; + } + VLOG(1) << "Hierarchical inter ring endpoints:" << ss.str(); + for (int i = 0; i < nccl_comm_num; i++) { + std::string nccl_var_name = + platform::GetHierarchicalInterNCCLVarName(i); + GenerateAndSend(&local_scope, dev_ctx, nccl_var_name, inter_endpoints); + } + } + + // hierarchical exter ncclid + if (exter_trainer_id == 0) { + std::ostringstream ss; + std::vector exter_endpoints; + ss << endpoint; + for (size_t i = inter_nranks; i < trainers.size(); i += inter_nranks) { + ss << ","; + exter_endpoints.push_back(trainers[i]); + ss << trainers[i]; + } + VLOG(1) << "Hierarchical exter ring endpoints:" << ss.str(); + for (int i = 0; i < nccl_comm_num; i++) { + std::string nccl_var_name = + platform::GetHierarchicalExterNCCLVarName(i); + GenerateAndSend(&local_scope, dev_ctx, nccl_var_name, exter_endpoints); + } } } private: void GenerateAndSend(framework::Scope* scope, - const platform::DeviceContext& dev_ctx) const { - auto var = scope->FindVar(NCCL_ID_VARNAME); - PADDLE_ENFORCE_NOT_NULL(var); + const platform::DeviceContext& dev_ctx, + const std::string& nccl_id_name, + const std::vector& endpoint_list) const { + auto var = scope->FindVar(nccl_id_name); + PADDLE_ENFORCE_NOT_NULL(var, "can't find nccl_id_var_name:%s", + nccl_id_name); auto id = var->GetMutable(); PADDLE_ENFORCE(platform::dynload::ncclGetUniqueId(id)); - std::vector endpoint_list = - Attr>("endpoint_list"); distributed::RPCClient* client = distributed::RPCClient::GetInstance(0); for (auto& ep : endpoint_list) { - VLOG(3) << "sending nccl id to " << ep; - client->AsyncSendVar(ep, dev_ctx, *scope, NCCL_ID_VARNAME); + VLOG(3) << "sending nccl_id_var:" << nccl_id_name << " to " << ep; + client->AsyncSendVar(ep, dev_ctx, *scope, nccl_id_name); } client->Wait(); for (auto& ep : endpoint_list) { @@ -75,9 +176,11 @@ class GenNCCLIdOp : public framework::OperatorBase { VLOG(3) << "sending completed..."; } - void GetIdByServer(framework::Scope* scope, - const platform::DeviceContext& dev_ctx) const { - std::string endpoint = Attr("endpoint"); + void GetIdByServer(const std::string& endpoint, framework::Scope* scope, + const platform::DeviceContext& dev_ctx, int nccl_comm_num, + bool use_hierarchical_allreduce, int trainer_id, + int inter_trainer_id, int exter_trainer_id) const { + // std::string endpoint = Attr("endpoint"); // NOTE: Can not use unique_ptr here because the default // deleter will call GRPC Server's base class's dtor and // that will cause a wired crash. @@ -98,10 +201,44 @@ class GenNCCLIdOp : public framework::OperatorBase { std::thread server_thread( std::bind(&distributed::RPCServer::StartServer, rpc_service.get())); - rpc_service->SetCond(distributed::kRequestSend); - VLOG(3) << "start getting nccl id from trainer 0..."; - rpc_service->WaitBarrier(distributed::kRequestSend); - VLOG(3) << "got nccl id and stop server..."; + for (int i = 0; i < nccl_comm_num; i++) { + rpc_service->SetCond(distributed::kRequestSend); + VLOG(3) << "trainer_id:" << trainer_id + << " start getting nccl id from trainer 0, nccl_comm_no:" << i; + rpc_service->WaitBarrier(distributed::kRequestSend); + rpc_service->ResetBarrierCounter(); + } + + if (use_hierarchical_allreduce) { + if (inter_trainer_id > 0) { + for (int i = 0; i < nccl_comm_num; i++) { + rpc_service->SetCond(distributed::kRequestSend); + VLOG(3) << "trainer_id:" << trainer_id + << ", inter_trainer_id:" << inter_trainer_id + << " start getting nccl id from inter_trainer:" << i; + rpc_service->WaitBarrier(distributed::kRequestSend); + rpc_service->ResetBarrierCounter(); + } + } + + if (exter_trainer_id > 0) { + for (int i = 0; i < nccl_comm_num; i++) { + rpc_service->SetCond(distributed::kRequestSend); + VLOG(3) + << "trainer_id:" << trainer_id + << ", exter_trainer_id:" << exter_trainer_id + << " start getting nccl id from exter_trainer 0, nccl_comm_no:" + << i; + rpc_service->WaitBarrier(distributed::kRequestSend); + rpc_service->ResetBarrierCounter(); + } + } + } + + VLOG(3) << "traier_id:" << trainer_id + << ", inter_trainer_id:" << inter_trainer_id + << ", exter_trainer_id:" << exter_trainer_id + << " got nccl id and stop server..."; rpc_service->ShutDown(); VLOG(3) << "rpc server stopped"; server_thread.join(); @@ -118,18 +255,26 @@ GenNCCLId operator For trainer 0: generate a new UniqueId and send it to all the other trainers. For trainer 1~n: start a gRPC server to get the UniqueId, once got, stop the server. )DOC"); - AddAttr("endpoint", - "(string), e.g. 127.0.0.1:6175 " - "current listen endpoint"); AddAttr>( - "endpoint_list", - "['trainer1_ip:port', 'trainer2_ip:port', ...] " - "list of trainer endpoints start from trainer 1") + "trainers", + "['trainer0_ip:port', 'trainer1_ip:port', ...] " + "list of all trainer endpoints") .SetDefault({}); AddAttr("trainer_id", - "(int default 0) " - "The index of the trainer in distributed training.") - .SetDefault(0); + "(int) " + "The index of the trainer in distributed training."); + AddAttr("nccl_comm_num", + "(int default 1) " + "The number of nccl communicator num.") + .SetDefault(1); + AddAttr("use_hierarchical_allreduce", + "(bool default false) " + "Wheter to use hierarchical allreduce.") + .SetDefault(false); + AddAttr("hierarchical_allreduce_inter_nranks", + "(int default 1) " + "Wheter to use hierarchical allreduce.") + .SetDefault(-1); } }; diff --git a/paddle/fluid/operators/distributed_ops/recv_op.cc b/paddle/fluid/operators/distributed_ops/recv_op.cc index 8e9846b1fc89953526149be3838103526d5c441b..b871859dbb142765bda7e6004206f20cdd77ae47 100644 --- a/paddle/fluid/operators/distributed_ops/recv_op.cc +++ b/paddle/fluid/operators/distributed_ops/recv_op.cc @@ -36,7 +36,7 @@ class RecvOp : public framework::OperatorBase { void RunImpl(const framework::Scope &scope, const platform::Place &place) const override { - bool do_not_run = Attr("do_not_run"); + int do_not_run = Attr("do_not_run"); if (do_not_run) { VLOG(3) << "recv do not run!"; return; @@ -132,7 +132,7 @@ This operator can get variables from server side. "(vector) " "the splited parameter varnames to be recved from pserver") .SetDefault(std::vector{}); - AddAttr("do_not_run", "if recv need to really run").SetDefault(false); + AddAttr("do_not_run", "if recv need to really run").SetDefault(0); } }; diff --git a/paddle/fluid/operators/elementwise/elementwise_add_op.cc b/paddle/fluid/operators/elementwise/elementwise_add_op.cc index 2b3fc06dcb79b8c6b46de7abf51bdb2c47acca1c..bf12d8a1a6de1374e8b99691f8f5713617321111 100644 --- a/paddle/fluid/operators/elementwise/elementwise_add_op.cc +++ b/paddle/fluid/operators/elementwise/elementwise_add_op.cc @@ -13,10 +13,48 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/elementwise/elementwise_add_op.h" +#include +#include #include "paddle/fluid/operators/elementwise/elementwise_op.h" -namespace ops = paddle::operators; + +namespace paddle { +namespace operators { + +class ElementwiseAddDoubleGradDescMaker + : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + std::unique_ptr op(new framework::OpDesc()); + op->SetType("elementwise_add_grad_grad"); + op->SetInput("Y", Input("Y")); + op->SetInput("DOut", Input(framework::GradVarName("Out"))); + op->SetInput("DDX", OutputGrad(framework::GradVarName("X"))); + op->SetInput("DDY", OutputGrad(framework::GradVarName("Y"))); + + op->SetAttrMap(Attrs()); + + op->SetOutput("DDOut", InputGrad(framework::GradVarName("Out"))); + return op; + } +}; + +} // namespace operators +} // namespace paddle + REGISTER_ELEMWISE_GRAD_MAKER(elementwise_add, Add); -REGISTER_ELEMWISE_EXPLICIT_OP(elementwise_add, "Add", "Out = X + Y"); +REGISTER_ELEMWISE_EXPLICIT_OP_WITHOUT_GRAD(elementwise_add, "Add", + "Out = X + Y"); + +namespace ops = paddle::operators; +REGISTER_OPERATOR(elementwise_add_grad, ops::ElementwiseOpExplicitGrad, + ops::ElementwiseGradOpInplace, + ops::ElementwiseGradNoBufVarsInference, + ops::ElementwiseAddDoubleGradDescMaker); +REGISTER_OPERATOR(elementwise_add_grad_grad, + ops::ElementwiseOpDoubleGradWithoutDXDY); REGISTER_OP_CPU_KERNEL( elementwise_add, @@ -30,3 +68,13 @@ REGISTER_OP_CPU_KERNEL( ops::ElementwiseAddGradKernel, ops::ElementwiseAddGradKernel, ops::ElementwiseAddGradKernel); +REGISTER_OP_CPU_KERNEL( + elementwise_add_grad_grad, + ops::ElementwiseAddDoubleGradKernel, + ops::ElementwiseAddDoubleGradKernel, + ops::ElementwiseAddDoubleGradKernel, + ops::ElementwiseAddDoubleGradKernel); diff --git a/paddle/fluid/operators/elementwise/elementwise_add_op.cu b/paddle/fluid/operators/elementwise/elementwise_add_op.cu index fed12785f47e1b8eea3f053712830901bee3bdc9..8320272b4b69ad24fbd1e94eb25fda53abe8c3fc 100644 --- a/paddle/fluid/operators/elementwise/elementwise_add_op.cu +++ b/paddle/fluid/operators/elementwise/elementwise_add_op.cu @@ -11,6 +11,7 @@ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ + #include "paddle/fluid/operators/elementwise/elementwise_add_op.h" #include "paddle/fluid/platform/float16.h" @@ -30,3 +31,9 @@ REGISTER_OP_CUDA_KERNEL( ops::ElementwiseAddGradKernel, ops::ElementwiseAddGradKernel, ops::ElementwiseAddGradKernel); +REGISTER_OP_CUDA_KERNEL( + elementwise_add_grad_grad, + ops::ElementwiseAddDoubleGradKernel, + ops::ElementwiseAddDoubleGradKernel, + ops::ElementwiseAddDoubleGradKernel, + ops::ElementwiseAddDoubleGradKernel); diff --git a/paddle/fluid/operators/elementwise/elementwise_add_op.h b/paddle/fluid/operators/elementwise/elementwise_add_op.h index 69f640ab6649df673f07ac0cef81bf80d16eb98d..7f8b0ffe92fd40d7944f05282c4edc8271547e00 100644 --- a/paddle/fluid/operators/elementwise/elementwise_add_op.h +++ b/paddle/fluid/operators/elementwise/elementwise_add_op.h @@ -40,25 +40,26 @@ template typename std::enable_if< std::is_floating_point::value && std::is_same::value>::type -elementwise_add(const framework::ExecutionContext &ctx, - const framework::Tensor *x, const framework::Tensor *y, - framework::Tensor *z) { - auto eigen_x = framework::EigenVector::Flatten(*x); - auto eigen_y = framework::EigenVector::Flatten(*y); - auto eigen_z = framework::EigenVector::Flatten(*z); - +elementwise_add_same_dims(const framework::ExecutionContext &ctx, + const framework::Tensor *x, + const framework::Tensor *y, framework::Tensor *z) { auto blas = math::GetBlas(ctx); - blas.VADD(x->numel(), eigen_x.data(), eigen_y.data(), eigen_z.data()); + blas.VADD(x->numel(), x->data(), y->data(), z->data()); } template typename std::enable_if< !std::is_floating_point::value || !std::is_same::value>::type -elementwise_add(const framework::ExecutionContext &ctx, - const framework::Tensor *x, const framework::Tensor *y, - framework::Tensor *z) { - default_elementwise_add(ctx, x, y, z); +elementwise_add_same_dims(const framework::ExecutionContext &ctx, + const framework::Tensor *x, + const framework::Tensor *y, framework::Tensor *z) { + auto eigen_x = framework::EigenVector::Flatten(*x); + auto eigen_y = framework::EigenVector::Flatten(*y); + auto eigen_z = framework::EigenVector::Flatten(*z); + + auto &place = *ctx.template device_context().eigen_device(); + eigen_z.device(place) = eigen_x + eigen_y; } template @@ -73,7 +74,7 @@ class ElementwiseAddKernel : public framework::OpKernel { auto dims_equal = x->dims() == y->dims(); if (dims_equal) { - elementwise_add(ctx, x, y, z); + elementwise_add_same_dims(ctx, x, y, z); } else { default_elementwise_add(ctx, x, y, z); } @@ -160,5 +161,31 @@ class ElementwiseAddGradKernel : public ElemwiseGradKernel { } }; +template +class ElementwiseAddDoubleGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + using Tensor = framework::Tensor; + + auto *y = ctx.Input("Y"); + auto *dout = ctx.Input("DOut"); + auto *ddx = ctx.Input("DDX"); + auto *ddy = ctx.Input("DDY"); + + auto *ddout = ctx.Output("DDOut"); + + // ddOut = ddx + ddy + if (ddout) { + Tensor ddx_safe, ddy_safe; + GetDoubleGradSafeTensor(ctx, dout, ddx, &ddx_safe); + GetDoubleGradSafeTensor(ctx, y, ddy, &ddy_safe); + + ddout->mutable_data(ctx.GetPlace()); + default_elementwise_add(ctx, &ddx_safe, &ddy_safe, + ddout); + } + } +}; + } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/elementwise/elementwise_div_op.cc b/paddle/fluid/operators/elementwise/elementwise_div_op.cc index 530a54b7ca186008bc8ec4b083254e65378ae619..6689823d4a2c9f2a082f0edfa564fdd9e51055b2 100644 --- a/paddle/fluid/operators/elementwise/elementwise_div_op.cc +++ b/paddle/fluid/operators/elementwise/elementwise_div_op.cc @@ -44,6 +44,31 @@ class ElementwiseDivGradOpDescMaker : public framework::SingleGradOpDescMaker { } }; +class ElementwiseDivDoubleGradDescMaker + : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + std::unique_ptr op(new framework::OpDesc()); + op->SetType("elementwise_div_grad_grad"); + op->SetInput("Y", Input("Y")); + op->SetInput("Out", Input("Out")); + op->SetInput("DDX", OutputGrad(framework::GradVarName("X"))); + op->SetInput("DDY", OutputGrad(framework::GradVarName("Y"))); + op->SetInput("DX", Output(framework::GradVarName("X"))); + + op->SetAttrMap(Attrs()); + + op->SetOutput(framework::GradVarName("Y"), InputGrad("Y")); + op->SetOutput("DOut", InputGrad("Out")); + op->SetOutput("DDOut", InputGrad(framework::GradVarName("Out"))); + + return op; + } +}; + } // namespace operators } // namespace paddle @@ -53,7 +78,9 @@ REGISTER_OPERATOR(elementwise_div, ops::ElementwiseOp, ops::ElementwiseDivOpMaker, ops::ElementwiseOpInferVarType, ops::ElementwiseDivGradOpDescMaker); -REGISTER_OPERATOR(elementwise_div_grad, ops::ElementwiseOpGrad); +REGISTER_OPERATOR(elementwise_div_grad, ops::ElementwiseOpGrad, + ops::ElementwiseDivDoubleGradDescMaker); +REGISTER_OPERATOR(elementwise_div_grad_grad, ops::ElementwiseDivOpDoubleGrad); REGISTER_OP_CPU_KERNEL( elementwise_div, @@ -67,3 +94,14 @@ REGISTER_OP_CPU_KERNEL( ops::ElementwiseDivGradKernel, ops::ElementwiseDivGradKernel, ops::ElementwiseDivGradKernel); + +REGISTER_OP_CPU_KERNEL( + elementwise_div_grad_grad, + ops::ElementwiseDivDoubleGradKernel, + ops::ElementwiseDivDoubleGradKernel, + ops::ElementwiseDivDoubleGradKernel, + ops::ElementwiseDivDoubleGradKernel); diff --git a/paddle/fluid/operators/elementwise/elementwise_div_op.cu b/paddle/fluid/operators/elementwise/elementwise_div_op.cu index ae669f5525443abe424109b6a6869e2ddaf52ba0..b38f84845b73cd2a57db26a58553e088ae7cdd9e 100644 --- a/paddle/fluid/operators/elementwise/elementwise_div_op.cu +++ b/paddle/fluid/operators/elementwise/elementwise_div_op.cu @@ -33,3 +33,13 @@ REGISTER_OP_CUDA_KERNEL( ops::ElementwiseDivGradKernel, ops::ElementwiseDivGradKernel); +REGISTER_OP_CUDA_KERNEL( + elementwise_div_grad_grad, + ops::ElementwiseDivDoubleGradKernel, + ops::ElementwiseDivDoubleGradKernel, + ops::ElementwiseDivDoubleGradKernel, + ops::ElementwiseDivDoubleGradKernel); diff --git a/paddle/fluid/operators/elementwise/elementwise_div_op.h b/paddle/fluid/operators/elementwise/elementwise_div_op.h index 0f0ad8637301772f073bca305b9196b9c7865daf..c604c9017ec9624c3e553d6bbf3b4c5ba5c483bf 100644 --- a/paddle/fluid/operators/elementwise/elementwise_div_op.h +++ b/paddle/fluid/operators/elementwise/elementwise_div_op.h @@ -14,8 +14,13 @@ limitations under the License. */ #pragma once +#include +#include "paddle/fluid/operators/elementwise/elementwise_mul_op.h" #include "paddle/fluid/operators/elementwise/elementwise_op.h" #include "paddle/fluid/operators/elementwise/elementwise_op_function.h" +#include "paddle/fluid/operators/elementwise/elementwise_sub_op.h" +#include "paddle/fluid/operators/reduce_ops/reduce_op.h" + namespace paddle { namespace operators { @@ -51,6 +56,13 @@ struct DivGradDY { } }; +template +struct DivDoubleDY { + HOSTDEVICE T operator()(T x, T y, T out, T dout) const { + return y * out * dout - x * dout; + } +}; + template class ElementwiseDivGradKernel : public ElemwiseGradKernel { public: @@ -72,5 +84,109 @@ class ElementwiseDivGradKernel : public ElemwiseGradKernel { } }; +class ElementwiseDivOpDoubleGrad : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + using Tensor = framework::Tensor; + + void InferShape(framework::InferShapeContext* ctx) const override { + auto y_grad_name = framework::GradVarName("Y"); + if (ctx->HasOutput("DOut")) { + ctx->ShareDim("DX", "DOut"); + ctx->ShareLoD("DX", "DOut"); + } + if (ctx->HasOutput(y_grad_name)) { + ctx->ShareDim("Y", y_grad_name); + ctx->ShareLoD("Y", y_grad_name); + } + if (ctx->HasOutput("DDOut")) { + ctx->ShareDim("DX", "DDOut"); + ctx->ShareLoD("DX", "DDOut"); + } + } + + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + auto input_data_type = ctx.Input("DDX")->type(); + +#ifdef PADDLE_WITH_MKLDNN + if (platform::CanMKLDNNBeUsed(ctx)) { + return framework::OpKernelType(input_data_type, ctx.GetPlace(), + framework::DataLayout::kMKLDNN, + framework::LibraryType::kMKLDNN); + } +#endif + return framework::OpKernelType(input_data_type, ctx.GetPlace()); + } +}; + +template +class ElementwiseDivDoubleGradKernel : public framework::OpKernel { + using Tensor = framework::Tensor; + + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* Y = ctx.Input("Y"); + auto* Out = ctx.Input("Out"); + auto* ddX = ctx.Input("DDX"); + auto* ddY = ctx.Input("DDY"); + auto* dX = ctx.Input("DX"); + + auto* dY = ctx.Output(framework::GradVarName("Y")); + auto* dOut = ctx.Output("DOut"); + auto* ddOut = ctx.Output("DDOut"); + + int axis = ctx.Attr("axis"); + + if (dY) dY->mutable_data(Y->dims(), ctx.GetPlace()); + if (dOut) dOut->mutable_data(Out->dims(), ctx.GetPlace()); + if (ddOut) ddOut->mutable_data(Out->dims(), ctx.GetPlace()); + + // ddX_safe == null ? 0 : ddX + // ddY_safe == null ? 0 : ddY + Tensor ddX_safe, ddY_safe; + GetDoubleGradSafeTensor(ctx, Out, ddX, &ddX_safe); + GetDoubleGradSafeTensor(ctx, Y, ddY, &ddY_safe); + + if (dOut) { + // dOut = - dX * ddY + default_elementwise_mul(ctx, dX, &ddY_safe, dOut); + auto& place = + *ctx.template device_context().eigen_device(); + auto dout = framework::EigenVector::Flatten(*dOut); + dout.device(place) = static_cast(-1) * dout; + } + + if (dY) { + // dX_div_Y = dX / Y; + auto& dev_ctx = ctx.template device_context(); + Tensor dX_div_Y = + ctx.AllocateTmpTensor(Out->dims(), dev_ctx); + ElementwiseComputeEx, DeviceContext, T>( + ctx, dX, Y, axis, DivFunctor(), &dX_div_Y); + + // NOTE(dengkaipeng): in the following ElemwiseGradCompute, for the + // first output tensor is nullptr, the branch to calculate first + // output tensor will not be activated, DivGradDx function will not + // be called and can be ignored, the first branch has little effect + // on running speed. + + // dY = Out * dX * ddY / Y - dX * ddX / Y + ElemwiseGradCompute, DivDoubleDY>( + ctx, ddX_safe, ddY_safe, *Out, dX_div_Y, axis, nullptr, dY, + DivGradDX(), DivDoubleDY()); + } + + if (ddOut) { + // ddOut = ddX / Y - Out * ddY / Y = (ddX - Out * ddY) / Y + default_elementwise_mul(ctx, Out, &ddY_safe, ddOut); + ElementwiseComputeEx, DeviceContext, T>( + ctx, &ddX_safe, ddOut, 0, SubFunctor(), ddOut); + ElementwiseComputeEx, DeviceContext, T>( + ctx, ddOut, Y, axis, DivFunctor(), ddOut); + } + } +}; + } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/elementwise/elementwise_mul_op.cc b/paddle/fluid/operators/elementwise/elementwise_mul_op.cc index d5e3300ac954aebf34a9c65fbca8de8fa2685932..0f6af96ff3dd1ba679af6e7b2d93d15eaeec0f2a 100644 --- a/paddle/fluid/operators/elementwise/elementwise_mul_op.cc +++ b/paddle/fluid/operators/elementwise/elementwise_mul_op.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/elementwise/elementwise_mul_op.h" +#include #include #include "paddle/fluid/operators/elementwise/elementwise_op.h" @@ -43,6 +44,30 @@ class ElementwiseMulOpMaker : public ElementwiseOpMaker { virtual std::string GetEquation() const { return "Out = X \\\\odot Y"; } }; +class ElementwiseMulDoubleGradDescMaker + : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + std::unique_ptr op(new framework::OpDesc()); + op->SetType("elementwise_mul_grad_grad"); + op->SetInput("X", Input("X")); + op->SetInput("Y", Input("Y")); + op->SetInput("DOut", Input(framework::GradVarName("Out"))); + op->SetInput("DDX", OutputGrad(framework::GradVarName("X"))); + op->SetInput("DDY", OutputGrad(framework::GradVarName("Y"))); + + op->SetAttrMap(Attrs()); + + op->SetOutput("DDOut", InputGrad(framework::GradVarName("Out"))); + op->SetOutput(framework::GradVarName("X"), InputGrad("X")); + op->SetOutput(framework::GradVarName("Y"), InputGrad("Y")); + return op; + } +}; + } // namespace operators } // namespace paddle @@ -50,7 +75,9 @@ namespace ops = paddle::operators; REGISTER_OPERATOR(elementwise_mul, ops::ElementwiseOp, ops::ElementwiseMulOpMaker, ops::ElementwiseOpInferVarType, ops::ElementwiseMulOpGradDescMaker); -REGISTER_OPERATOR(elementwise_mul_grad, ops::ElementwiseOpGrad); +REGISTER_OPERATOR(elementwise_mul_grad, ops::ElementwiseOpGrad, + ops::ElementwiseMulDoubleGradDescMaker); +REGISTER_OPERATOR(elementwise_mul_grad_grad, ops::ElementwiseOpDoubleGrad); REGISTER_OP_CPU_KERNEL( elementwise_mul, @@ -64,3 +91,13 @@ REGISTER_OP_CPU_KERNEL( ops::ElementwiseMulGradKernel, ops::ElementwiseMulGradKernel, ops::ElementwiseMulGradKernel); +REGISTER_OP_CPU_KERNEL( + elementwise_mul_grad_grad, + ops::ElementwiseMulDoubleGradKernel, + ops::ElementwiseMulDoubleGradKernel, + ops::ElementwiseMulDoubleGradKernel, + ops::ElementwiseMulDoubleGradKernel); diff --git a/paddle/fluid/operators/elementwise/elementwise_mul_op.cu b/paddle/fluid/operators/elementwise/elementwise_mul_op.cu index e36cc8f9f28d0ed3d3693e0a38d8bb17fa4ba25d..d18c7e66f10a0a7c4e63fdb2262228727591daee 100644 --- a/paddle/fluid/operators/elementwise/elementwise_mul_op.cu +++ b/paddle/fluid/operators/elementwise/elementwise_mul_op.cu @@ -11,6 +11,7 @@ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ + #include "paddle/fluid/operators/elementwise/elementwise_mul_op.h" #include "paddle/fluid/platform/float16.h" @@ -88,3 +89,9 @@ REGISTER_OP_CUDA_KERNEL( ops::ElementwiseMulGradKernel, ops::ElementwiseMulGradKernel, ops::ElementwiseMulGradKernel); +REGISTER_OP_CUDA_KERNEL( + elementwise_mul_grad_grad, + ops::ElementwiseMulDoubleGradKernel, + ops::ElementwiseMulDoubleGradKernel, + ops::ElementwiseMulDoubleGradKernel, + ops::ElementwiseMulDoubleGradKernel); diff --git a/paddle/fluid/operators/elementwise/elementwise_mul_op.h b/paddle/fluid/operators/elementwise/elementwise_mul_op.h index 7a7a3989c047ae379cc14e2f783662db99239445..105707b803e205cf5718ed7305d2e6882c76973e 100644 --- a/paddle/fluid/operators/elementwise/elementwise_mul_op.h +++ b/paddle/fluid/operators/elementwise/elementwise_mul_op.h @@ -38,22 +38,26 @@ template typename std::enable_if< std::is_floating_point::value && std::is_same::value>::type -elementwise_mul(const framework::ExecutionContext& ctx, - const framework::Tensor* x, const framework::Tensor* y, - framework::Tensor* z) { +elementwise_mul_same_dims(const framework::ExecutionContext& ctx, + const framework::Tensor* x, + const framework::Tensor* y, framework::Tensor* z) { auto blas = math::GetBlas(ctx); - blas.VMUL(x->numel(), x->data(), y->data(), - z->mutable_data(ctx.GetPlace())); + blas.VMUL(x->numel(), x->data(), y->data(), z->data()); } template typename std::enable_if< !std::is_floating_point::value || !std::is_same::value>::type -elementwise_mul(const framework::ExecutionContext& ctx, - const framework::Tensor* x, const framework::Tensor* y, - framework::Tensor* z) { - default_elementwise_mul(ctx, x, y, z); +elementwise_mul_same_dims(const framework::ExecutionContext& ctx, + const framework::Tensor* x, + const framework::Tensor* y, framework::Tensor* z) { + auto eigen_x = framework::EigenVector::Flatten(*x); + auto eigen_y = framework::EigenVector::Flatten(*y); + auto eigen_z = framework::EigenVector::Flatten(*z); + + auto& place = *ctx.template device_context().eigen_device(); + eigen_z.device(place) = eigen_x * eigen_y; } template @@ -88,7 +92,7 @@ class ElementwiseMulKernel : public framework::OpKernel { z->mutable_data(ctx.GetPlace()); if (x.numel() == y->numel()) { - elementwise_mul(ctx, &x, y, z); + elementwise_mul_same_dims(ctx, &x, y, z); } else { default_elementwise_mul(ctx, &x, y, z); } @@ -123,5 +127,56 @@ class ElementwiseMulGradKernel : public ElemwiseGradKernel { ctx, *x, *y, *out, *dout, axis, dx, dy, MulGradDX(), MulGradDY()); } }; + +template +class ElementwiseMulDoubleGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + using Tensor = framework::Tensor; + + auto* x = ctx.Input("X"); + auto* y = ctx.Input("Y"); + auto* dout = ctx.Input("DOut"); + auto* ddx = ctx.Input("DDX"); + auto* ddy = ctx.Input("DDY"); + + auto* dx = ctx.Output(framework::GradVarName("X")); + auto* dy = ctx.Output(framework::GradVarName("Y")); + auto* ddout = ctx.Output("DDOut"); + + if (ddout) ddout->mutable_data(ctx.GetPlace()); + + // dx = dout * ddy + // dy = dout * ddx + Tensor ddx_safe, ddy_safe; + GetDoubleGradSafeTensor(ctx, x, ddx, &ddx_safe); + GetDoubleGradSafeTensor(ctx, y, ddy, &ddy_safe); + int axis = ctx.Attr("axis"); + ElemwiseGradCompute, MulGradDY>( + ctx, ddx_safe, ddy_safe, *dout, *dout, axis, dx, dy, MulGradDX(), + MulGradDY()); + + // ddout = ddx * y + x * ddy + if (ddout) { + if (ddx && ddy) { + Tensor ddout_tmp; + ddout_tmp.mutable_data(ddout->dims(), ctx.GetPlace()); + + default_elementwise_mul(ctx, ddx, y, ddout); + default_elementwise_mul(ctx, x, ddy, &ddout_tmp); + + auto& place = + *ctx.template device_context().eigen_device(); + auto ddout_t = framework::EigenVector::Flatten(*ddout); + auto ddout_tmp_t = framework::EigenVector::Flatten(ddout_tmp); + ddout_t.device(place) = ddout_t + ddout_tmp_t; + } else { + if (ddx) default_elementwise_mul(ctx, ddx, y, ddout); + if (ddy) default_elementwise_mul(ctx, x, ddy, ddout); + } + } + } +}; + } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/elementwise/elementwise_op.h b/paddle/fluid/operators/elementwise/elementwise_op.h index 5ec335972a02a3a6911274ba7609f50665f3d0e0..c251cc722703cbd6388e911c6899415e4240cfda 100644 --- a/paddle/fluid/operators/elementwise/elementwise_op.h +++ b/paddle/fluid/operators/elementwise/elementwise_op.h @@ -212,6 +212,71 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel { } }; +class ElementwiseOpDoubleGrad : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + using Tensor = framework::Tensor; + + void InferShape(framework::InferShapeContext *ctx) const override { + auto x_grad_name = framework::GradVarName("X"); + auto y_grad_name = framework::GradVarName("Y"); + if (ctx->HasOutput(x_grad_name)) { + ctx->ShareDim("X", x_grad_name); + ctx->ShareLoD("X", x_grad_name); + } + if (ctx->HasOutput(y_grad_name)) { + ctx->ShareDim("Y", y_grad_name); + ctx->ShareLoD("Y", y_grad_name); + } + if (ctx->HasOutput("DDOut")) { + ctx->ShareDim("DOut", "DDOut"); + ctx->ShareLoD("DOut", "DDOut"); + } + } + + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext &ctx) const override { + auto input_data_type = ctx.Input("DOut")->type(); + +#ifdef PADDLE_WITH_MKLDNN + if (platform::CanMKLDNNBeUsed(ctx)) { + return framework::OpKernelType(input_data_type, ctx.GetPlace(), + framework::DataLayout::kMKLDNN, + framework::LibraryType::kMKLDNN); + } +#endif + return framework::OpKernelType(input_data_type, ctx.GetPlace()); + } +}; + +class ElementwiseOpDoubleGradWithoutDXDY + : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + using Tensor = framework::Tensor; + + void InferShape(framework::InferShapeContext *ctx) const override { + if (ctx->HasOutput("DDOut")) { + ctx->ShareDim("DOut", "DDOut"); + ctx->ShareLoD("DOut", "DDOut"); + } + } + + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext &ctx) const override { + auto input_data_type = ctx.Input("DOut")->type(); + +#ifdef PADDLE_WITH_MKLDNN + if (platform::CanMKLDNNBeUsed(ctx)) { + return framework::OpKernelType(input_data_type, ctx.GetPlace(), + framework::DataLayout::kMKLDNN, + framework::LibraryType::kMKLDNN); + } +#endif + return framework::OpKernelType(input_data_type, ctx.GetPlace()); + } +}; + // For Add, Sub op, the X, Out is not needed. class ElementwiseOpExplicitGrad : public ElementwiseOpGrad { public: @@ -322,3 +387,16 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(ElementwiseGradNoBufVarsInference, "Y"); ::paddle::operators::ElementwiseOpExplicitGrad, \ ::paddle::operators::ElementwiseGradOpInplace, \ ::paddle::operators::ElementwiseGradNoBufVarsInference) + +#define REGISTER_ELEMWISE_EXPLICIT_OP_WITHOUT_GRAD(op_type, op_name, equation) \ + class __ElemwiseOp##op_type##Maker__ \ + : public ::paddle::operators::ElementwiseOpMaker { \ + protected: \ + virtual std::string GetName() const { return op_name; } \ + virtual std::string GetEquation() const { return equation; } \ + }; \ + REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp, \ + __ElemwiseOp##op_type##Maker__, \ + ::paddle::operators::ElementwiseOpInferVarType, \ + op_type##GradMaker, \ + ::paddle::operators::ElementwiseOpInplace); diff --git a/paddle/fluid/operators/elementwise/elementwise_op_function.h b/paddle/fluid/operators/elementwise/elementwise_op_function.h index 2e91ec84848b0f491dca0a271d9326e3c37632ea..2b108efef4a34b5e03bd55cd59adfbfb0df67e22 100644 --- a/paddle/fluid/operators/elementwise/elementwise_op_function.h +++ b/paddle/fluid/operators/elementwise/elementwise_op_function.h @@ -1005,24 +1005,24 @@ template struct FusedElemwiseAndActGradNoBroadcast { HOSTDEVICE void operator()(size_t i) { + T x_val = x_[i]; + T y_val = y_[i]; + T out_val = out_[i]; + T dout_val = dout_[i]; + T intermediate_out_val = UseIntermediateOut + ? intermediate_out_[i] + : dx_op_.GetIntermediateOut(x_val, y_val); if (dx_ != nullptr) { - dx_[i] = UseIntermediateOut - ? dx_op_.UseIntermediateOut( - x_[i], y_[i], intermediate_out_[i], out_[i], dout_[i]) - : dx_op_.Recompute(x_[i], y_[i], out_[i], dout_[i]); + dx_[i] = dx_op_.UseIntermediateOut(x_val, y_val, intermediate_out_val, + out_val, dout_val); } if (dy_ != nullptr) { - dy_[i] = UseIntermediateOut - ? dy_op_.UseIntermediateOut( - x_[i], y_[i], intermediate_out_[i], out_[i], dout_[i]) - : dy_op_.Recompute(x_[i], y_[i], out_[i], dout_[i]); + dy_[i] = dy_op_.UseIntermediateOut(x_val, y_val, intermediate_out_val, + out_val, dout_val); } if (dintermediate_ != nullptr) { - dintermediate_[i] = - UseIntermediateOut - ? dintermediate_op_.UseIntermediateOut( - x_[i], intermediate_out_[i], out_[i], dout_[i]) - : dintermediate_op_.Recompute(x_[i], y_[i], out_[i], dout_[i]); + dintermediate_[i] = dintermediate_op_.UseIntermediateOut( + x_val, intermediate_out_val, out_val, dout_val); } } @@ -1636,5 +1636,21 @@ void FusedElemwiseAndActComputeEx(const framework::ExecutionContext &ctx, } } } + +template +static inline void GetDoubleGradSafeTensor( + const framework::ExecutionContext &ctx, const framework::Tensor *x, + const framework::Tensor *ddx, framework::Tensor *ddx_safe) { + if (ddx) { + *ddx_safe = *ddx; + } else { + auto &dev_ctx = ctx.template device_context(); + *ddx_safe = ctx.AllocateTmpTensor(x->dims(), dev_ctx); + math::SetConstant set_zero; + set_zero(ctx.template device_context(), ddx_safe, + static_cast(0)); + } +} + } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/elementwise/elementwise_sub_op.cc b/paddle/fluid/operators/elementwise/elementwise_sub_op.cc index 04c87c1b2ac398f8f75265c80bef5326aea15dce..b1ec10ea86c836acce6cf7d83bfc53866de06afd 100644 --- a/paddle/fluid/operators/elementwise/elementwise_sub_op.cc +++ b/paddle/fluid/operators/elementwise/elementwise_sub_op.cc @@ -13,10 +13,48 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/elementwise/elementwise_sub_op.h" +#include +#include #include "paddle/fluid/operators/elementwise/elementwise_op.h" + +namespace paddle { +namespace operators { + +class ElementwiseSubDoubleGradDescMaker + : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + std::unique_ptr op(new framework::OpDesc()); + op->SetType("elementwise_sub_grad_grad"); + op->SetInput("Y", Input("Y")); + op->SetInput("DOut", Input(framework::GradVarName("Out"))); + op->SetInput("DDX", OutputGrad(framework::GradVarName("X"))); + op->SetInput("DDY", OutputGrad(framework::GradVarName("Y"))); + + op->SetAttrMap(Attrs()); + + op->SetOutput("DDOut", InputGrad(framework::GradVarName("Out"))); + return op; + } +}; + +} // namespace operators +} // namespace paddle + namespace ops = paddle::operators; REGISTER_ELEMWISE_GRAD_MAKER(elementwise_sub, Sub); -REGISTER_ELEMWISE_EXPLICIT_OP(elementwise_sub, "Sub", "Out = X - Y"); +REGISTER_ELEMWISE_EXPLICIT_OP_WITHOUT_GRAD(elementwise_sub, "Sub", + "Out = X - Y"); + +REGISTER_OPERATOR(elementwise_sub_grad, ops::ElementwiseOpExplicitGrad, + ops::ElementwiseGradOpInplace, + ops::ElementwiseGradNoBufVarsInference, + ops::ElementwiseSubDoubleGradDescMaker); +REGISTER_OPERATOR(elementwise_sub_grad_grad, + ops::ElementwiseOpDoubleGradWithoutDXDY); REGISTER_OP_CPU_KERNEL( elementwise_sub, @@ -30,3 +68,13 @@ REGISTER_OP_CPU_KERNEL( ops::ElementwiseSubGradKernel, ops::ElementwiseSubGradKernel, ops::ElementwiseSubGradKernel); +REGISTER_OP_CPU_KERNEL( + elementwise_sub_grad_grad, + ops::ElementwiseSubDoubleGradKernel, + ops::ElementwiseSubDoubleGradKernel, + ops::ElementwiseSubDoubleGradKernel, + ops::ElementwiseSubDoubleGradKernel); diff --git a/paddle/fluid/operators/elementwise/elementwise_sub_op.cu b/paddle/fluid/operators/elementwise/elementwise_sub_op.cu index f2adf1c83730c317cd4f4d2a4039c0f94da9df7b..52fad7fd04b0083c81089899d4dab80853441ca7 100644 --- a/paddle/fluid/operators/elementwise/elementwise_sub_op.cu +++ b/paddle/fluid/operators/elementwise/elementwise_sub_op.cu @@ -33,3 +33,13 @@ REGISTER_OP_CUDA_KERNEL( ops::ElementwiseSubGradKernel, ops::ElementwiseSubGradKernel); +REGISTER_OP_CUDA_KERNEL( + elementwise_sub_grad_grad, + ops::ElementwiseSubDoubleGradKernel, + ops::ElementwiseSubDoubleGradKernel, + ops::ElementwiseSubDoubleGradKernel, + ops::ElementwiseSubDoubleGradKernel); diff --git a/paddle/fluid/operators/elementwise/elementwise_sub_op.h b/paddle/fluid/operators/elementwise/elementwise_sub_op.h index 770323fe5a8fe7c1051b418b2541ab4c669635b4..5049d587b582a71981f45a72dc5bfc133dadb52d 100644 --- a/paddle/fluid/operators/elementwise/elementwise_sub_op.h +++ b/paddle/fluid/operators/elementwise/elementwise_sub_op.h @@ -68,5 +68,33 @@ class ElementwiseSubGradKernel : public ElemwiseGradKernel { ctx, *x, *y, *out, *dout, axis, dx, dy, SubGradDX(), SubGradDY()); } }; + +template +class ElementwiseSubDoubleGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + using Tensor = framework::Tensor; + + auto* y = ctx.Input("Y"); + auto* dout = ctx.Input("DOut"); + auto* ddx = ctx.Input("DDX"); + auto* ddy = ctx.Input("DDY"); + + auto* ddout = ctx.Output("DDOut"); + + // DDOut = ddx - ddy + if (ddout) { + Tensor ddx_safe, ddy_safe; + GetDoubleGradSafeTensor(ctx, dout, ddx, &ddx_safe); + GetDoubleGradSafeTensor(ctx, y, ddy, &ddy_safe); + + ddout->mutable_data(ctx.GetPlace()); + int axis = ctx.Attr("axis"); + ElementwiseComputeEx, DeviceContext, T>( + ctx, &ddx_safe, &ddy_safe, axis, SubFunctor(), ddout); + } + } +}; + } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc b/paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc index 6a6741d8fc54d22addca91b75dfabf5950c1a35a..88cda1cd66868933de99b3b864ea98627df1e304 100644 --- a/paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc +++ b/paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc @@ -16,6 +16,7 @@ limitations under the License. */ #include "paddle/fluid/operators/elementwise/elementwise_add_op.h" #include "paddle/fluid/operators/elementwise/elementwise_op_function.h" +#include "paddle/fluid/framework/data_layout_transform.h" #include "paddle/fluid/platform/mkldnn_helper.h" namespace paddle { @@ -53,12 +54,44 @@ class EltwiseAddMKLDNNKernel : public framework::OpKernel { // Execute default elementwise_add operator when // broadcast operations need to performed. if (x_dims != y_dims_untrimed) { + Tensor _x; + mkldnn::memory::format format; + std::vector src_x_tz = framework::vectorize2int(x_dims); + + if ((src_x_tz.size() == 3 && + x->format() != (format = memory::format::ncw)) || + (src_x_tz.size() == 4 && + x->format() != (format = memory::format::nchw)) || + (src_x_tz.size() == 5 && + x->format() != (format = memory::format::ncdhw))) { + _x.Resize(x_dims); + auto user_x_memory_pd = memory::primitive_desc( + {{src_x_tz}, memory::data_type::f32, x->format()}, mkldnn_engine); + auto x_memory_pd = memory::primitive_desc( + {{src_x_tz}, memory::data_type::f32, format}, mkldnn_engine); + auto size = x_memory_pd.get_size(); + _x.mutable_data(ctx.GetPlace(), size); + auto user_x_memory = + memory(user_x_memory_pd, paddle::platform::to_void_cast(x_data)); + auto x_memory = memory(x_memory_pd, + paddle::platform::to_void_cast(_x.data())); + + auto x_reorder = reorder(user_x_memory, x_memory); + + std::vector pipeline; + pipeline.push_back(x_reorder); + stream(stream::kind::eager).submit(pipeline).wait(); + } else { + format = x->format(); + _x.ShareDataWith(*x); + } + auto sum_func = [](T a, T b) -> T { return a + b; }; TransformFunctor functor( - x, y, z, + &_x, y, z, ctx.template device_context(), sum_func); @@ -78,7 +111,7 @@ class EltwiseAddMKLDNNKernel : public framework::OpKernel { functor.RunMidWise(n, pre, post); } z->set_layout(DataLayout::kMKLDNN); - z->set_format(x->format()); + z->set_format(format); } else { PADDLE_ENFORCE(x->layout() == DataLayout::kMKLDNN && x->format() != memory::format::format_undef, diff --git a/paddle/fluid/operators/expand_op.cc b/paddle/fluid/operators/expand_op.cc index fcb2be93635eeaeaae25c3a845fd06aa1a73e2e7..e15f848c23df7ca25dd15b9595b18b62cb7c2790 100644 --- a/paddle/fluid/operators/expand_op.cc +++ b/paddle/fluid/operators/expand_op.cc @@ -14,6 +14,7 @@ limitations under the License. */ #include "paddle/fluid/operators/expand_op.h" #include +#include #include namespace paddle { @@ -30,9 +31,12 @@ class ExpandOp : public framework::OperatorWithKernel { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null."); - std::vector expand_times = - ctx->Attrs().Get>("expand_times"); auto x_dims = ctx->GetInputDim("X"); + std::vector expand_times(x_dims.size(), -1); + + if (!ctx->HasInputs("expand_times_tensor")) { + expand_times = ctx->Attrs().Get>("expand_times"); + } PADDLE_ENFORCE_EQ(static_cast(x_dims.size()), expand_times.size(), "The number of Attr(expand_times)'s value must be equal " @@ -42,15 +46,11 @@ class ExpandOp : public framework::OperatorWithKernel { std::vector out_shape(x_dims.size()); for (size_t i = 0; i < expand_times.size(); ++i) { - PADDLE_ENFORCE_GE(expand_times[i], 1, - "Each value of Attr(expand_times) should not be " - "less than 1."); - out_shape[i] = x_dims[i] * expand_times[i]; - } - - // set the first dim to -1 in compile time - if (!ctx->IsRuntime() && x_dims[0] < 0) { - out_shape[0] = x_dims[0]; + if (x_dims[i] == -1 || expand_times[i] == -1) { + out_shape[i] = -1; + } else { + out_shape[i] = x_dims[i] * expand_times[i]; + } } ctx->SetOutputDim("Out", framework::make_ddim(out_shape)); @@ -58,6 +58,23 @@ class ExpandOp : public framework::OperatorWithKernel { ctx->ShareLoD("X", "Out"); } } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + return framework::OpKernelType(ctx.Input("X")->type(), + ctx.device_context()); + } + + framework::OpKernelType GetKernelTypeForVar( + const std::string& var_name, const Tensor& tensor, + const framework::OpKernelType& expected_kernel_type) const override { + if (var_name == "expand_times_tensor") { + return expected_kernel_type; + } + return framework::OpKernelType(expected_kernel_type.data_type_, + tensor.place(), tensor.layout()); + } }; class ExpandOpMaker : public framework::OpProtoAndCheckerMaker { @@ -66,6 +83,9 @@ class ExpandOpMaker : public framework::OpProtoAndCheckerMaker { AddInput("X", "(Tensor, default Tensor). A tensor with rank in [1, 6]." "X is the input to be expanded."); + AddInput("expand_times_tensor", "(Tensor Tensor), epxand times for X") + .AsDuplicable() + .AsDispensable(); AddOutput("Out", "(Tensor, default Tensor). A tensor with rank in [1, 6]." "The rank of Output(Out) have the same with Input(X). " @@ -73,7 +93,8 @@ class ExpandOpMaker : public framework::OpProtoAndCheckerMaker { "to size of the corresponding dimension of Input(X) multiplying " "the corresponding value given by Attr(expand_times)."); AddAttr>("expand_times", - "Expand times number for each dimension."); + "Expand times number for each dimension.") + .SetDefault({}); AddComment(R"DOC( Expand operator tiles the input by given times number. You should set times number for each dimension by providing attribute 'expand_times'. The rank of X @@ -113,6 +134,7 @@ class ExpandGradOp : public framework::OperatorWithKernel { auto x_dims = ctx->GetInputDim("X"); std::vector expand_times = ctx->Attrs().Get>("expand_times"); + auto out_dims = ctx->GetInputDim(framework::GradVarName("Out")); size_t start_pos = 0u; @@ -137,6 +159,23 @@ class ExpandGradOp : public framework::OperatorWithKernel { ctx->SetOutputDim(x_grad_name, x_dims); } } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + return framework::OpKernelType(ctx.Input("X")->type(), + ctx.device_context()); + } + + framework::OpKernelType GetKernelTypeForVar( + const std::string& var_name, const Tensor& tensor, + const framework::OpKernelType& expected_kernel_type) const override { + if (var_name == "expand_times_tensor") { + return expected_kernel_type; + } + return framework::OpKernelType(expected_kernel_type.data_type_, + tensor.place(), tensor.layout()); + } }; class ExpandGradOpDescMaker : public framework::SingleGradOpDescMaker { @@ -150,6 +189,7 @@ class ExpandGradOpDescMaker : public framework::SingleGradOpDescMaker { op->SetInput("X", Input("X")); op->SetInput(framework::GradVarName("Out"), OutputGrad("Out")); op->SetOutput(framework::GradVarName("X"), InputGrad("X")); + op->SetInput("expand_times_tensor", Input("expand_times_tensor")); op->SetAttrMap(Attrs()); return op; } diff --git a/paddle/fluid/operators/expand_op.h b/paddle/fluid/operators/expand_op.h index 339408249771d99434ba87ab95b41f0884f2950f..8153987d6c721c39544ff02a8adc925e3f01fd14 100644 --- a/paddle/fluid/operators/expand_op.h +++ b/paddle/fluid/operators/expand_op.h @@ -48,6 +48,29 @@ limitations under the License. */ namespace paddle { namespace operators { +inline std::vector get_expand_times( + const framework::ExecutionContext& ctx) { + auto list_expand_times_tensor = + ctx.MultiInput("expand_times_tensor"); + if (list_expand_times_tensor.size() > 0) { + // get tensor from + std::vector vec_epxand_times; + for (size_t i = 0; i < list_expand_times_tensor.size(); ++i) { + auto tensor = list_expand_times_tensor[i]; + if (platform::is_gpu_place(tensor->place())) { + framework::Tensor temp; + TensorCopySync(*tensor, platform::CPUPlace(), &temp); + vec_epxand_times.push_back(*temp.data()); + } else { + vec_epxand_times.push_back(*tensor->data()); + } + } + + return vec_epxand_times; + } else { + return ctx.Attr>("expand_times"); + } +} using Tensor = framework::Tensor; template { template void Expand(const framework::ExecutionContext& context) const { auto* in0 = context.Input("X"); - auto& expand_times = context.Attr>("expand_times"); + + auto in_dims = in0->dims(); + auto expand_times = get_expand_times(context); auto* out0 = context.Output("Out"); Eigen::DSizes bcast_dims; for (size_t i = 0; i < expand_times.size(); ++i) { bcast_dims[i] = expand_times[i]; } + + framework::DDim out_dims(in_dims); + for (size_t i = 0; i < expand_times.size(); ++i) { + out_dims[i] *= expand_times[i]; + } + + out0->Resize(out_dims); auto x = EigenTensor::From(*in0); out0->mutable_data(context.GetPlace()); auto y = EigenTensor::From(*out0); @@ -94,7 +126,8 @@ class ExpandGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* in0 = context.Input("X"); - auto& expand_times = context.Attr>("expand_times"); + // auto& expand_times = context.Attr>("expand_times"); + auto expand_times = get_expand_times(context); auto x_dims = in0->dims(); // 1. reshape_dims_vec is the broadcast parameter. For each dimension i, // if expand_times[i] > 1 and x_dims[i] > 1, i will be splitted to two diff --git a/paddle/fluid/operators/fake_quantize_op.cc b/paddle/fluid/operators/fake_quantize_op.cc index 25ca1f7e0a0086b803d48aa892b0888e0d5635b1..034f3c7dcebf906e600b9a6a651a1c857ddc4189 100644 --- a/paddle/fluid/operators/fake_quantize_op.cc +++ b/paddle/fluid/operators/fake_quantize_op.cc @@ -68,6 +68,23 @@ struct ClipAndFakeQuantFunctor { template struct ClipAndFakeQuantFunctor; +template +struct ClipAndFakeQuantDequantFunctor { + void operator()(const platform::CPUDeviceContext& ctx, + const framework::Tensor& in, const framework::Tensor& scale, + const int bin_cnt, framework::Tensor* out) { + T s = scale.data()[0]; + platform::Transform trans; + trans(ctx, in.data(), in.data() + in.numel(), + out->mutable_data(ctx.GetPlace()), ClipFunctor(-s, s)); + auto out_e = framework::EigenVector::Flatten(*out); + out_e.device(*ctx.eigen_device()) = + (s / bin_cnt) * (bin_cnt / s * out_e).round(); + } +}; +template struct ClipAndFakeQuantDequantFunctor; + template struct ChannelClipAndFakeQuantFunctor { void operator()(const platform::CPUDeviceContext& ctx, @@ -324,24 +341,26 @@ $$Out = round(X/scale * range)$$ } }; -class FakeQuantizeMovingAverageAbsMaxOp : public framework::OperatorWithKernel { +class FakeQuantOrWithDequantMovingAverageAbsMaxOp + : public framework::OperatorWithKernel { public: - FakeQuantizeMovingAverageAbsMaxOp(const std::string& type, - const framework::VariableNameMap& inputs, - const framework::VariableNameMap& outputs, - const framework::AttributeMap& attrs) + FakeQuantOrWithDequantMovingAverageAbsMaxOp( + const std::string& type, const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, + const framework::AttributeMap& attrs) : OperatorWithKernel(type, inputs, outputs, attrs) {} void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), + "Input(X) of FakeQuantOrWithDequantMovingAverageAbsMaxOp " + "should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Output(Out) of FakeQuantOrWithDequantMovingAverageAbsMaxOp " + "should not be null."); PADDLE_ENFORCE( - ctx->HasInput("X"), - "Input(X) of FakeQuantizeMovingAverageAbsMaxOp should not be null."); - PADDLE_ENFORCE( - ctx->HasOutput("Out"), - "Output(Out) of FakeQuantizeMovingAverageAbsMaxOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("OutScale"), - "Output(OutScale) of FakeQuantizeMovingAverageAbsMaxOp " - "should not be null"); + ctx->HasOutput("OutScale"), + "Output(OutScale) of FakeQuantOrWithDequantMovingAverageAbsMaxOp " + "should not be null"); if (ctx->HasOutput("OutState")) { ctx->SetOutputDim("OutState", {1}); } @@ -361,7 +380,7 @@ class FakeQuantizeMovingAverageAbsMaxOp : public framework::OperatorWithKernel { } }; -class FakeQuantizeMovingAverageAbsMaxOpMaker +class FakeQuantOrWithDequantMovingAverageAbsMaxOpMaker : public framework::OpProtoAndCheckerMaker { public: void Make() override { @@ -386,12 +405,19 @@ class FakeQuantizeMovingAverageAbsMaxOpMaker "for training. Some layers may run faster when this is true.") .SetDefault(false); AddComment(R"DOC( -FakeQuantize operator is used in static quantization. +This is a Base Op which support FakeQuantMovingAverageAbsMaxOp and FakeQuantDequantMovingAverageAbsMaxOp +FakeQuantMovingAverageAbsMaxOp operator is used in static quantization. $$scale = (moving\_rate*accum+max(abs(x)))/(moving\_rate*state+1)$$ $$range = 2^{bit\_length - 1} - 1$$ $$Out = round(X/scale * range)$$ +FakeQuantDequantMovingAverageAbsMaxOp operator do the moving_average_abs_max op quant and then dequant. + +$$scale = (moving\_rate*accum+max(abs(x)))/(moving\_rate*state+1)$$ +$$range = 2^{bit\_length - 1} - 1$$ +$$Out = round(X/scale * range) * scale / range$$ + )DOC"); } }; @@ -477,11 +503,21 @@ REGISTER_OP_CPU_KERNEL(fake_quantize_range_abs_max, ops::FakeQuantizeRangeAbsMaxKernel); REGISTER_OPERATOR(fake_quantize_moving_average_abs_max, - ops::FakeQuantizeMovingAverageAbsMaxOp, - ops::FakeQuantizeMovingAverageAbsMaxOpMaker, + ops::FakeQuantOrWithDequantMovingAverageAbsMaxOp, + ops::FakeQuantOrWithDequantMovingAverageAbsMaxOpMaker, paddle::framework::EmptyGradOpMaker); + REGISTER_OP_CPU_KERNEL(fake_quantize_moving_average_abs_max, ops::FakeQuantizeMovingAverageAbsMaxKernel); + +REGISTER_OPERATOR(fake_quantize_dequantize_moving_average_abs_max, + ops::FakeQuantOrWithDequantMovingAverageAbsMaxOp, + ops::FakeQuantOrWithDequantMovingAverageAbsMaxOpMaker, + paddle::framework::EmptyGradOpMaker); +REGISTER_OP_CPU_KERNEL( + fake_quantize_dequantize_moving_average_abs_max, + ops::FakeQuantizeDequantizeMovingAverageAbsMaxKernel); + REGISTER_OPERATOR(fake_channel_wise_quantize_abs_max, ops::FakeChannelWiseQuantizeAbsMaxOp, ops::FakeChannelWiseQuantizeAbsMaxOpMaker, diff --git a/paddle/fluid/operators/fake_quantize_op.cu b/paddle/fluid/operators/fake_quantize_op.cu index 6e1d40cac765eec93f6e3a0425ccf0329a246649..e9a7201bc0826414ec4adbd3bf2804db013a4571 100644 --- a/paddle/fluid/operators/fake_quantize_op.cu +++ b/paddle/fluid/operators/fake_quantize_op.cu @@ -129,6 +129,23 @@ __global__ void ClipAndQuantKernel(const T* in, const T* scale, } } +template +__global__ void ClipAndQuantDequantKernel(const T* in, const T* scale, + const int bin_cnt, const int n, + T* out) { + int bid = threadIdx.x + blockIdx.x * blockDim.x; + int tid = threadIdx.x; + + T s = scale[0]; + for (int i = bid; i < n; i += blockDim.x * gridDim.x) { + T x = in[i]; + T v = x > s ? s : x; + v = v < -s ? -s : v; + v = bin_cnt / s * v; + out[i] = round(v) * s / bin_cnt; + } +} + template struct ClipAndFakeQuantFunctor { void operator()(const platform::CUDADeviceContext& ctx, @@ -149,6 +166,27 @@ struct ClipAndFakeQuantFunctor { template struct ClipAndFakeQuantFunctor; +template +struct ClipAndFakeQuantDequantFunctor { + void operator()(const platform::CUDADeviceContext& ctx, + const framework::Tensor& in, const framework::Tensor& scale, + const int bin_cnt, framework::Tensor* out) { + int num = in.numel(); + int block = 1024; + int grid = (block - 1 + num) / block; + + const T* in_data = in.data(); + const T* scale_data = scale.data(); + T* out_data = out->mutable_data(ctx.GetPlace()); + + ClipAndQuantDequantKernel<<>>( + in_data, scale_data, bin_cnt, num, out_data); + } +}; + +template struct ClipAndFakeQuantDequantFunctor; + template __global__ void ChannelClipAndQuantKernel(const T* in, const T* scale, const int bin_cnt, const int n, @@ -226,8 +264,8 @@ struct FindRangeAbsMaxFunctor { T* out_scale_data = out_scale->mutable_data(gpu_place); framework::Tensor need_find_max, out_size; - int* find_max = need_find_max.mutable_data(gpu_place); - int* out_size_data = out_size.mutable_data(gpu_place); + int* find_max = need_find_max.mutable_data({1}, gpu_place); + int* out_size_data = out_size.mutable_data({1}, gpu_place); FindRangeAbsMaxAndFillArray<<<1, 1, 0, ctx.stream()>>>( cur_scale.data(), last_scale.data(), iter.data(), @@ -302,3 +340,6 @@ REGISTER_OP_CUDA_KERNEL( ops::FakeQuantizeMovingAverageAbsMaxKernel); REGISTER_OP_CUDA_KERNEL(moving_average_abs_max_scale, ops::MovingAverageAbsMaxScaleKernel); +REGISTER_OP_CUDA_KERNEL( + fake_quantize_dequantize_moving_average_abs_max, + ops::FakeQuantizeDequantizeMovingAverageAbsMaxKernel); diff --git a/paddle/fluid/operators/fake_quantize_op.h b/paddle/fluid/operators/fake_quantize_op.h index 87bcece582442e7336049d65bcabc87eadd52342..422d99dd433055bdc91c4a25e5eab36259011df8 100644 --- a/paddle/fluid/operators/fake_quantize_op.h +++ b/paddle/fluid/operators/fake_quantize_op.h @@ -35,6 +35,13 @@ struct ClipAndFakeQuantFunctor { framework::Tensor* out); }; +template +struct ClipAndFakeQuantDequantFunctor { + void operator()(const DeviceContext& ctx, const framework::Tensor& in, + const framework::Tensor& scale, const int bin_cnt, + framework::Tensor* out); +}; + template struct FindRangeAbsMaxFunctor { void operator()(const DeviceContext& ctx, const framework::Tensor& cur_scale, @@ -150,8 +157,13 @@ class FakeQuantizeRangeAbsMaxKernel : public framework::OpKernel { }; template -class FakeQuantizeMovingAverageAbsMaxKernel : public framework::OpKernel { +class FakeMovingAverageAbsMaxKernelBase : public framework::OpKernel { public: + ~FakeMovingAverageAbsMaxKernelBase() {} + virtual void RunClipFunctor(const DeviceContext& dev_ctx, + const framework::Tensor& in, + const framework::Tensor& in_scale, int bin_cnt, + framework::Tensor* out) const = 0; void Compute(const framework::ExecutionContext& context) const override { auto* in = context.Input("X"); auto* in_scale = context.Input("InScale"); @@ -165,8 +177,7 @@ class FakeQuantizeMovingAverageAbsMaxKernel : public framework::OpKernel { // testing if (is_test) { - ClipAndFakeQuantFunctor()(dev_ctx, *in, *in_scale, - bin_cnt, out); + RunClipFunctor(dev_ctx, *in, *in_scale, bin_cnt, out); return; } @@ -193,8 +204,31 @@ class FakeQuantizeMovingAverageAbsMaxKernel : public framework::OpKernel { dev_ctx, *in_accum, *in_state, cur_scale_data, moving_rate, out_state, out_accum, out_scale); - ClipAndFakeQuantFunctor()(dev_ctx, *in, *out_scale, - bin_cnt, out); + RunClipFunctor(dev_ctx, *in, *out_scale, bin_cnt, out); + } +}; + +template +class FakeQuantizeMovingAverageAbsMaxKernel + : public FakeMovingAverageAbsMaxKernelBase { + public: + void RunClipFunctor(const DeviceContext& dev_ctx, const framework::Tensor& in, + const framework::Tensor& in_scale, int bin_cnt, + framework::Tensor* out) const override { + ClipAndFakeQuantFunctor()(dev_ctx, in, in_scale, bin_cnt, + out); + } +}; + +template +class FakeQuantizeDequantizeMovingAverageAbsMaxKernel + : public FakeMovingAverageAbsMaxKernelBase { + public: + void RunClipFunctor(const DeviceContext& dev_ctx, const framework::Tensor& in, + const framework::Tensor& in_scale, int bin_cnt, + framework::Tensor* out) const override { + ClipAndFakeQuantDequantFunctor()(dev_ctx, in, in_scale, + bin_cnt, out); } }; diff --git a/paddle/fluid/operators/fused/fused_elemwise_activation_op.cc b/paddle/fluid/operators/fused/fused_elemwise_activation_op.cc index 0fbf564b7efaeb5c62be60557960eabfe45850c6..1cd6c40aa0540f5e5c9ea4b3e3e771dcc827eccf 100644 --- a/paddle/fluid/operators/fused/fused_elemwise_activation_op.cc +++ b/paddle/fluid/operators/fused/fused_elemwise_activation_op.cc @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/fused/fused_elemwise_activation_op.h" +#include +#include namespace paddle { namespace operators { @@ -48,7 +50,10 @@ bool InputXCanBeAbsent(const std::vector &functor_list) { * out. */ static bool IsSupportedCompound(const std::vector &functors) { - static std::unordered_set unary_fun = {"scale", "relu"}; + PADDLE_ENFORCE_EQ(functors.size(), 2UL); + + static std::unordered_set unary_fun = {"scale", "relu", "tanh", + "sigmoid"}; static std::unordered_set binary_fun = {"elementwise_add", "elementwise_mul"}; diff --git a/paddle/fluid/operators/fused/fused_elemwise_activation_op.h b/paddle/fluid/operators/fused/fused_elemwise_activation_op.h index 01dc2dbfd61cc88f72174233382aa49f61c9b60f..7cb753211eab328680ed78c9f3aa5409f487dc41 100644 --- a/paddle/fluid/operators/fused/fused_elemwise_activation_op.h +++ b/paddle/fluid/operators/fused/fused_elemwise_activation_op.h @@ -255,6 +255,27 @@ static void RunFunctors(const framework::ExecutionContext &ctx, paddle::operators::math::ScaleFunctor>( ctx, paddle::operators::math::MulFunctor(), paddle::operators::math::ScaleFunctor(scale), in_x, in_y, outputs); + } else if (funcs_str == "tanh,elementwise_add") { + // Z = Unary(Binary(X, Y)) + RunUnaryCompoundFunctors, + paddle::operators::math::AddFunctor>( + ctx, paddle::operators::math::TanhFunctor(), + paddle::operators::math::AddFunctor(), in_x, in_y, outputs); + } else if (funcs_str == "elementwise_mul,tanh") { + // Z = Binary(X, Unary(Y)) + RunBinaryCompoundFunctor, + paddle::operators::math::TanhFunctor>( + ctx, paddle::operators::math::MulFunctor(), + paddle::operators::math::TanhFunctor(), in_x, in_y, outputs); + } else if (funcs_str == "elementwise_mul,sigmoid") { + // Z = Binary(X, Unary(Y)) + RunBinaryCompoundFunctor, + paddle::operators::math::SigmoidFunctor>( + ctx, paddle::operators::math::MulFunctor(), + paddle::operators::math::SigmoidFunctor(), in_x, in_y, outputs); } else { PADDLE_THROW("%s has not been implemented.", funcs_str); } @@ -293,6 +314,7 @@ static void RunGradFunctors( paddle::operators::math::AddGradFunctor(), in_x, in_y, in_out, in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out); } else if (funcs_str == "elementwise_add_grad,relu_grad") { + // The backward of Z = Binary(X, Unary(Y)) RunBinaryCompoundGradFunctors< DeviceContext, T, paddle::operators::math::AddGradFunctor, paddle::operators::math::ReluFunctor, @@ -302,6 +324,7 @@ static void RunGradFunctors( paddle::operators::math::ReluGradFunctor(), in_x, in_y, in_out, in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out); } else if (funcs_str == "relu_grad,elementwise_add_grad") { + // The backward of Z = Unary(Binary(X, Y)) RunUnaryCompoundGradFunctors< DeviceContext, T, paddle::operators::math::ReluGradFunctor, paddle::operators::math::AddFunctor, @@ -321,6 +344,36 @@ static void RunGradFunctors( paddle::operators::math::ScaleFunctor(scale), paddle::operators::math::ScaleGradFunctor(scale), in_x, in_y, in_out, in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out); + } else if (funcs_str == "tanh_grad,elementwise_add_grad") { + // The backward of Z = Unary(Binary(X, Y)) + RunUnaryCompoundGradFunctors< + DeviceContext, T, paddle::operators::math::TanhGradFunctor, + paddle::operators::math::AddFunctor, + paddle::operators::math::AddGradFunctor, InPlace>( + ctx, paddle::operators::math::TanhGradFunctor(), + paddle::operators::math::AddFunctor(), + paddle::operators::math::AddGradFunctor(), in_x, in_y, in_out, + in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out); + } else if (funcs_str == "elementwise_mul_grad,tanh_grad") { + // The backward of Z = Binary(X, Unary(Y)) + RunBinaryCompoundGradFunctors< + DeviceContext, T, paddle::operators::math::MulGradFunctor, + paddle::operators::math::TanhFunctor, + paddle::operators::math::TanhGradFunctor, InPlace>( + ctx, paddle::operators::math::MulGradFunctor(), + paddle::operators::math::TanhFunctor(), + paddle::operators::math::TanhGradFunctor(), in_x, in_y, in_out, + in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out); + } else if (funcs_str == "elementwise_mul_grad,sigmoid_grad") { + // The backward of Z = Binary(X, Unary(Y)) + RunBinaryCompoundGradFunctors< + DeviceContext, T, paddle::operators::math::MulGradFunctor, + paddle::operators::math::SigmoidFunctor, + paddle::operators::math::SigmoidGradFunctor, InPlace>( + ctx, paddle::operators::math::MulGradFunctor(), + paddle::operators::math::SigmoidFunctor(), + paddle::operators::math::SigmoidGradFunctor(), in_x, in_y, in_out, + in_intermediate_out, in_out_grad, x_grad, y_grad, d_intermediate_out); } else { PADDLE_THROW("%s has not been implemented.", funcs_str); } diff --git a/paddle/fluid/operators/gather.cu.h b/paddle/fluid/operators/gather.cu.h index 5bc2e63757f19c1dc8a7d41fae9621a2816ff31b..fff817fbd022eebb318cc0c1763e363737bf321e 100644 --- a/paddle/fluid/operators/gather.cu.h +++ b/paddle/fluid/operators/gather.cu.h @@ -26,14 +26,15 @@ using platform::DeviceContext; for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ i += blockDim.x * gridDim.x) -template -__global__ void GatherCUDAKernel(const T* params, const int* indices, T* output, - size_t index_size, size_t slice_size) { +template +__global__ void GatherCUDAKernel(const T* params, const IndexT* indices, + T* output, size_t index_size, + size_t slice_size) { CUDA_1D_KERNEL_LOOP(i, index_size * slice_size) { int indices_i = i / slice_size; int slice_i = i - indices_i * slice_size; // offset inside the slice - int gather_i = indices[indices_i]; - int params_i = gather_i * slice_size + slice_i; + IndexT gather_i = indices[indices_i]; + IndexT params_i = gather_i * slice_size + slice_i; *(output + i) = *(params + params_i); } } @@ -42,10 +43,10 @@ __global__ void GatherCUDAKernel(const T* params, const int* indices, T* output, * A thin wrapper on gpu tensor * Return a new tensor from source tensor, gathered according to index * input[src]: type-T source Tensor - * input[index]: type-int index Tensor (1-D) + * input[index]: type-IndexT index Tensor (1-D) * return: output tensor */ -template +template void GPUGather(const platform::DeviceContext& ctx, const Tensor& src, const Tensor& index, Tensor* output) { // PADDLE_ENFORCE(platform::is_gpu_place(place)); @@ -64,15 +65,14 @@ void GPUGather(const platform::DeviceContext& ctx, const Tensor& src, for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; const T* p_src = src.data(); - // why must be int? - const int* p_index = index.data(); + const IndexT* p_index = index.data(); T* p_output = output->data(); int block = 512; int n = slice_size * index_size; int grid = (n + block - 1) / block; - GatherCUDAKernel<<< + GatherCUDAKernel<<< grid, block, 0, reinterpret_cast(ctx).stream()>>>( p_src, p_index, p_output, index_size, slice_size); diff --git a/paddle/fluid/operators/gather.h b/paddle/fluid/operators/gather.h index dc08ee5efacde5e232d751b13aaf11f51237634a..1e02c036e350a5a7c9bf87591c15ff976aaa8dcb 100644 --- a/paddle/fluid/operators/gather.h +++ b/paddle/fluid/operators/gather.h @@ -30,10 +30,10 @@ using framework::Tensor; * A thin wrapper for gathering on cpu tensor * Return a new tensor from source tensor, gathered according to index * input[src]: type-T source Tensor - * input[index]: type-int index Tensor (1-D) + * input[index]: type-IndexT index Tensor (1-D) * return: output tensor */ -template +template void CPUGather(const platform::DeviceContext& ctx, const Tensor& src, const Tensor& index, Tensor* output) { PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace())); @@ -45,7 +45,7 @@ void CPUGather(const platform::DeviceContext& ctx, const Tensor& src, auto src_dims = src.dims(); const T* p_src = src.data(); - const int* p_index = index.data(); + const IndexT* p_index = index.data(); T* p_output = output->data(); // slice size @@ -55,7 +55,7 @@ void CPUGather(const platform::DeviceContext& ctx, const Tensor& src, const size_t slice_bytes = slice_size * sizeof(T); for (int64_t i = 0; i < index_size; ++i) { - int index_ = p_index[i]; + IndexT index_ = p_index[i]; memcpy(p_output + i * slice_size, p_src + index_ * slice_size, slice_bytes); } } diff --git a/paddle/fluid/operators/gather_op.cc b/paddle/fluid/operators/gather_op.cc index 91f3818f2165c91eef88921859afe5703bd65685..cbabd59cf634f09c0a55d3822995b4d0f5f170ee 100644 --- a/paddle/fluid/operators/gather_op.cc +++ b/paddle/fluid/operators/gather_op.cc @@ -74,6 +74,13 @@ class GatherOpMaker : public framework::OpProtoAndCheckerMaker { AddInput("X", "The source input of gather op"); AddInput("Index", "The index input of gather op"); AddOutput("Out", "The output of gather op"); + AddAttr( + "overwrite", + "(bool, default: False) " + "In backward process, calc the grad when has same index," + "If true, update the grad using the overwrite mode in same index," + "If false, using the accumulate mode in same index.") + .SetDefault(true); AddComment(R"DOC( Gather Operator. diff --git a/paddle/fluid/operators/gather_op.cu b/paddle/fluid/operators/gather_op.cu index 490ba9a585ee8fac82a9e1178f506a6d39e5fd1c..061f92c76c32fbc599bd8f5d32bb110c276d748f 100644 --- a/paddle/fluid/operators/gather_op.cu +++ b/paddle/fluid/operators/gather_op.cu @@ -32,7 +32,20 @@ class GatherOpCUDAKernel : public framework::OpKernel { output->mutable_data(ctx.GetPlace()); if (x->numel() == 0) return; - GPUGather(ctx.device_context(), *x, *index, output); + const auto &index_type = index->type(); + bool index_type_match = index_type == framework::proto::VarType::INT32 || + index_type == framework::proto::VarType::INT64; + PADDLE_ENFORCE( + index_type_match, + "Index holds the wrong type, it holds %s, but desires to be %s or %s", + paddle::framework::DataTypeToString(index_type), + paddle::framework::DataTypeToString(framework::proto::VarType::INT32), + paddle::framework::DataTypeToString(framework::proto::VarType::INT64)); + if (index_type == framework::proto::VarType::INT32) { + GPUGather(ctx.device_context(), *x, *index, output); + } else if (index_type == framework::proto::VarType::INT64) { + GPUGather(ctx.device_context(), *x, *index, output); + } } }; @@ -42,7 +55,7 @@ class GatherGradOpCUDAKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext &ctx) const override { PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), "This kernel only runs on GPU device."); - auto *Index = ctx.Input("Index"); + auto *index = ctx.Input("Index"); auto *dX = ctx.Output(framework::GradVarName("X")); auto *dO = ctx.Input(framework::GradVarName("Out")); @@ -52,7 +65,23 @@ class GatherGradOpCUDAKernel : public framework::OpKernel { .eigen_device(); dxt.device(place) = dxt.constant(static_cast(0)); if (dO->numel() == 0) return; - GPUScatterAssign(ctx.device_context(), *dO, *Index, dX); + + const auto &index_type = index->type(); + bool index_type_match = index_type == framework::proto::VarType::INT32 || + index_type == framework::proto::VarType::INT64; + PADDLE_ENFORCE( + index_type_match, + "Index holds the wrong type, it holds %s, but desires to be %s or %s", + paddle::framework::DataTypeToString(index_type), + paddle::framework::DataTypeToString(framework::proto::VarType::INT32), + paddle::framework::DataTypeToString(framework::proto::VarType::INT64)); + if (index_type == framework::proto::VarType::INT32) { + GPUScatterAssign(ctx, *dO, *index, dX, + ctx.Attr("overwrite")); + } else if (index_type == framework::proto::VarType::INT64) { + GPUScatterAssign(ctx, *dO, *index, dX, + ctx.Attr("overwrite")); + } } }; diff --git a/paddle/fluid/operators/gather_op.h b/paddle/fluid/operators/gather_op.h index 2e18298cf8e34d5f70369c89b3b3b2a9ced0ce62..852790a4c63c85d89dd19a870fa84991798219eb 100644 --- a/paddle/fluid/operators/gather_op.h +++ b/paddle/fluid/operators/gather_op.h @@ -36,7 +36,21 @@ class GatherOpKernel : public framework::OpKernel { output->mutable_data(ctx.GetPlace()); if (x->numel() == 0) return; - CPUGather(ctx.device_context(), *x, *index, output); + + const auto &index_type = index->type(); + bool index_type_match = index_type == framework::proto::VarType::INT32 || + index_type == framework::proto::VarType::INT64; + PADDLE_ENFORCE( + index_type_match, + "Index holds the wrong type, it holds %s, but desires to be %s or %s", + paddle::framework::DataTypeToString(index_type), + paddle::framework::DataTypeToString(framework::proto::VarType::INT32), + paddle::framework::DataTypeToString(framework::proto::VarType::INT64)); + if (index_type == framework::proto::VarType::INT32) { + CPUGather(ctx.device_context(), *x, *index, output); + } else if (index_type == framework::proto::VarType::INT64) { + CPUGather(ctx.device_context(), *x, *index, output); + } } }; @@ -47,7 +61,7 @@ class GatherGradientOpKernel : public framework::OpKernel { PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), "This kernel only runs on CPU."); - auto *Index = ctx.Input("Index"); + auto *index = ctx.Input("Index"); auto *dX = ctx.Output(framework::GradVarName("X")); auto *dO = ctx.Input(framework::GradVarName("Out")); @@ -57,7 +71,30 @@ class GatherGradientOpKernel : public framework::OpKernel { .eigen_device(); dxt.device(place) = dxt.constant(static_cast(0)); if (dO->numel() == 0) return; - ScatterAssign(ctx.device_context(), *dO, *Index, dX); + bool overwrite = ctx.Attr("overwrite"); + + const auto &index_type = index->type(); + bool index_type_match = index_type == framework::proto::VarType::INT32 || + index_type == framework::proto::VarType::INT64; + PADDLE_ENFORCE( + index_type_match, + "Index holds the wrong type, it holds %s, but desires to be %s or %s", + paddle::framework::DataTypeToString(index_type), + paddle::framework::DataTypeToString(framework::proto::VarType::INT32), + paddle::framework::DataTypeToString(framework::proto::VarType::INT64)); + if (index_type == framework::proto::VarType::INT32) { + if (overwrite) { + ScatterAssign(ctx.device_context(), *dO, *index, dX); + } else { + ScatterAssignAdd(ctx, *dO, *index, dX); + } + } else if (index_type == framework::proto::VarType::INT64) { + if (overwrite) { + ScatterAssign(ctx.device_context(), *dO, *index, dX); + } else { + ScatterAssignAdd(ctx, *dO, *index, dX); + } + } } }; diff --git a/paddle/fluid/operators/gru_op.h b/paddle/fluid/operators/gru_op.h index 45c769ee37260bf912ebc848d58019557f4adc07..bcca992e2b426677e32d2c82e853d79534d114a6 100644 --- a/paddle/fluid/operators/gru_op.h +++ b/paddle/fluid/operators/gru_op.h @@ -144,7 +144,7 @@ class GRUGradKernel : public framework::OpKernel { Tensor hidden_prev_grad_t = batch_hidden_grad.Slice(bstart_pre, bstart); gru_grad.prev_out_grad = hidden_prev_grad_t.data(); } - + gru_value.output_value = nullptr; math::GRUUnitGradFunctor::compute( dev_ctx, gru_value, gru_grad, frame_size, cur_batch_size, active_node, active_gate, origin_mode); diff --git a/paddle/fluid/operators/im2sequence_op.cc b/paddle/fluid/operators/im2sequence_op.cc index 44fd95edef253b814a166f724ca67fcafe979b99..0fa7322fbd65c85f574c18f822e2c189c8f87646 100644 --- a/paddle/fluid/operators/im2sequence_op.cc +++ b/paddle/fluid/operators/im2sequence_op.cc @@ -34,7 +34,7 @@ class Im2SequenceOp : public framework::OperatorWithKernel { PADDLE_ENFORCE_EQ(in_dim.size(), 4, "Input(X) format must be 4D tensor, eg., NCHW."); - int img_channels = in_dim[1]; + auto img_channels = in_dim[1]; auto kernels = ctx->Attrs().Get>("kernels"); auto strides = ctx->Attrs().Get>("strides"); diff --git a/paddle/fluid/operators/im2sequence_op.h b/paddle/fluid/operators/im2sequence_op.h index 4a9942819414d552eb69bd0b30b66aab76a2dbf4..9c9069b722763d0ec0d39d2f6fb35477c7578f30 100644 --- a/paddle/fluid/operators/im2sequence_op.h +++ b/paddle/fluid/operators/im2sequence_op.h @@ -113,9 +113,10 @@ class Im2SequenceKernel : public framework::OpKernel { paddings[2], strides[0]); int output_width = Im2SeqOutputSize(img_width, kernels[1], paddings[1], paddings[3], strides[1]); - out->mutable_data({batch_size * output_height * output_width, - img_channels * kernels[0] * kernels[1]}, - ctx.GetPlace()); + out->mutable_data( + {static_cast(batch_size) * output_height * output_width, + static_cast(img_channels) * kernels[0] * kernels[1]}, + ctx.GetPlace()); const std::vector dilations({1, 1}); auto out_dims = out->dims(); out->Resize({batch_size, out->numel() / batch_size}); diff --git a/paddle/fluid/operators/interpolate_op.h b/paddle/fluid/operators/interpolate_op.h index 5fd42809dfec6dd821c9b27bc97d61de94b5d326..652aec9a5385335ea2649c208a7b174aad744a71 100644 --- a/paddle/fluid/operators/interpolate_op.h +++ b/paddle/fluid/operators/interpolate_op.h @@ -11,6 +11,7 @@ #pragma once #include +#include #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/operators/math/math_function.h" @@ -57,7 +58,17 @@ static void BilinearInterpolation(const Tensor& input, Tensor* output, auto input_t = EigenTensor::From(input); auto output_t = EigenTensor::From(*output); bool align_flag = (align_mode == 0 && !align_corners); - for (int k = 0; k < out_h; k++) { // loop for images + + std::vector vy_n, vy_s; + std::vector vd_n, vd_s; + vy_n.reserve(out_h); + vy_s.reserve(out_h); + vd_n.reserve(out_h); + vd_s.reserve(out_h); +#ifdef PADDLE_WITH_MKLML +#pragma omp parallel for +#endif + for (int k = 0; k < out_h; k++) { int y_n = align_flag ? static_cast(ratio_h * (k + 0.5) - 0.5) : static_cast(ratio_h * k); y_n = (y_n > 0) ? y_n : 0; @@ -65,24 +76,53 @@ static void BilinearInterpolation(const Tensor& input, Tensor* output, float d_n = align_flag ? ratio_h * (k + 0.5) - 0.5 - y_n : ratio_h * k - y_n; float d_s = 1.f - d_n; + { + vy_n[k] = y_n; + vy_s[k] = y_s; + vd_n[k] = d_n; + vd_s[k] = d_s; + } + } - for (int l = 0; l < out_w; l++) { - int x_w = (align_mode == 0 && !align_corners) - ? static_cast(ratio_w * (l + 0.5) - 0.5) - : static_cast(ratio_w * l); - x_w = (x_w > 0) ? x_w : 0; - int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1); - float d_w = - align_flag ? ratio_w * (l + 0.5) - 0.5 - x_w : ratio_w * l - x_w; - float d_e = 1.f - d_w; + std::vector vx_w, vx_e; + std::vector vd_w, vd_e; + vx_w.reserve(out_w); + vx_e.reserve(out_w); + vd_w.reserve(out_w); + vd_e.reserve(out_w); +#ifdef PADDLE_WITH_MKLML +#pragma omp parallel for +#endif + for (int l = 0; l < out_w; l++) { + int x_w = (align_mode == 0 && !align_corners) + ? static_cast(ratio_w * (l + 0.5) - 0.5) + : static_cast(ratio_w * l); + x_w = (x_w > 0) ? x_w : 0; + int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1); + float d_w = + align_flag ? ratio_w * (l + 0.5) - 0.5 - x_w : ratio_w * l - x_w; + float d_e = 1.f - d_w; + { + vx_w[l] = x_w; + vx_e[l] = x_e; + vd_w[l] = d_w; + vd_e[l] = d_e; + } + } - for (int i = 0; i < n; i++) { // loop for batches - for (int j = 0; j < c; j++) { // loop for channels +#ifdef PADDLE_WITH_MKLML +#pragma omp parallel for collapse(4) +#endif + for (int i = 0; i < n; i++) { // loop for batches + for (int j = 0; j < c; j++) { // loop for channels + for (int k = 0; k < out_h; k++) { // loop for images + for (int l = 0; l < out_w; l++) { // bilinear interpolation - output_t(i, j, k, l) = input_t(i, j, y_n, x_w) * d_s * d_e + - input_t(i, j, y_s, x_w) * d_n * d_e + - input_t(i, j, y_n, x_e) * d_s * d_w + - input_t(i, j, y_s, x_e) * d_n * d_w; + T out_t = input_t(i, j, vy_n[k], vx_w[l]) * vd_s[k] * vd_e[l] + + input_t(i, j, vy_s[k], vx_w[l]) * vd_n[k] * vd_e[l] + + input_t(i, j, vy_n[k], vx_e[l]) * vd_s[k] * vd_w[l] + + input_t(i, j, vy_s[k], vx_e[l]) * vd_n[k] * vd_w[l]; + output_t(i, j, k, l) = out_t; } } } diff --git a/paddle/fluid/operators/load_combine_op.h b/paddle/fluid/operators/load_combine_op.h index 8f620ba7d2f1c2797ad4fd76a16af9aeee9c2806..45a155af852baa36b0fe6631e5c427694736c89f 100644 --- a/paddle/fluid/operators/load_combine_op.h +++ b/paddle/fluid/operators/load_combine_op.h @@ -65,7 +65,10 @@ class LoadCombineOpKernel : public framework::OpKernel { auto *tensor = out_vars[i]->GetMutable(); // Error checking - PADDLE_ENFORCE(static_cast(*buffer), "Cannot read more"); + PADDLE_ENFORCE( + static_cast(*buffer), + "There is a problem with loading model parameters. " + "Please check whether the model file is complete or damaged."); // Get data from fin to tensor DeserializeFromStream(*buffer, tensor, dev_ctx); diff --git a/paddle/fluid/operators/lod_tensor_to_array_op.cc b/paddle/fluid/operators/lod_tensor_to_array_op.cc index 61e342737045616112d51b7753939286a31dc6cd..962822f33e6e69bfd1ee90a100473ba1c8185495 100644 --- a/paddle/fluid/operators/lod_tensor_to_array_op.cc +++ b/paddle/fluid/operators/lod_tensor_to_array_op.cc @@ -131,9 +131,7 @@ class LoDTensorToArrayOp : public framework::OperatorBase { } } - auto &outputs = *const_cast(scope) - .Var() - ->GetMutable>(); + std::map outputs; for (size_t i = 0; i < max_seq_len; ++i) { auto &ranges = copy_ranges[i]; diff --git a/paddle/fluid/operators/math/compound_functors.h b/paddle/fluid/operators/math/compound_functors.h index 7aba4a917cdea50f95bcc7627f707257606fc927..6a43215bf52a9b231a47241d1bb27695da031957 100644 --- a/paddle/fluid/operators/math/compound_functors.h +++ b/paddle/fluid/operators/math/compound_functors.h @@ -74,6 +74,8 @@ struct BinaryCompoundGradDxFunctor { return dout * d_binary_fun_.Dx(x, intermediate_out); } + inline HOSTDEVICE T GetIntermediateOut(T x, T y) { return unary_fun_(y); } + private: DBinaryFun d_binary_fun_; UnaryFun unary_fun_; @@ -105,6 +107,8 @@ struct BinaryCompoundGradDyFunctor { } } + inline HOSTDEVICE T GetIntermediateOut(T x, T y) { return unary_fun_(y); } + private: DBinaryFun d_binary_fun_; UnaryFun unary_fun_; @@ -143,6 +147,8 @@ struct UnaryCompoundGradDxFunctor { return base * d_binary_fun_.Dx(x, y); } + inline HOSTDEVICE T GetIntermediateOut(T x, T y) { return binary_fun_(x, y); } + private: DUnaryFun d_unary_fun_; BinaryFun binary_fun_; @@ -181,6 +187,8 @@ struct UnaryCompoundGradDyFunctor { return base * d_binary_fun_.Dy(x, y); } + inline HOSTDEVICE T GetIntermediateOut(T x, T y) { return binary_fun_(x, y); } + private: DUnaryFun d_unary_fun_; BinaryFun binary_fun_; @@ -203,6 +211,8 @@ struct BinaryCompoundGradDIntermedaiteOutFunctor { return dout * d_binary_fun_.Dy(x, intermediate_out); } + inline HOSTDEVICE T GetIntermediateOut(T x, T y) { return unary_fun_(y); } + private: DBinaryFun d_binary_fun_; UnaryFun unary_fun_; @@ -232,6 +242,8 @@ struct UnaryCompoundGradDIntermediateFunctor { } } + inline HOSTDEVICE T GetIntermediateOut(T x, T y) { return binary_fun_(x, y); } + private: DUnaryFun d_unary_fun_; BinaryFun binary_fun_; diff --git a/paddle/fluid/operators/math/concat_and_split.cu b/paddle/fluid/operators/math/concat_and_split.cu index e925e7bb5917c9433c3c79b9a21a41b4d48a5ba0..153e6117227bf9fd273f83f8e64f859a54380053 100644 --- a/paddle/fluid/operators/math/concat_and_split.cu +++ b/paddle/fluid/operators/math/concat_and_split.cu @@ -24,9 +24,9 @@ namespace operators { namespace math { template -__global__ void ConcatKernel(T** inputs, const int* input_cols, int col_size, - const int output_rows, const int output_cols, - T* output) { +__global__ void ConcatKernel(const T** inputs, const int* input_cols, + int col_size, const int output_rows, + const int output_cols, T* output) { int tid_x = blockIdx.x * blockDim.x + threadIdx.x; int curr_segment = 0; int curr_offset = input_cols[0]; @@ -41,7 +41,7 @@ __global__ void ConcatKernel(T** inputs, const int* input_cols, int col_size, int local_col = tid_x - curr_offset; int segment_width = curr_col_offset - curr_offset; - T* input_ptr = inputs[curr_segment]; + const T* input_ptr = inputs[curr_segment]; int tid_y = blockIdx.y * blockDim.y + threadIdx.y; for (; tid_y < output_rows; tid_y += blockDim.y * gridDim.y) output[tid_y * output_cols + tid_x] = @@ -50,14 +50,14 @@ __global__ void ConcatKernel(T** inputs, const int* input_cols, int col_size, } template -__global__ void ConcatKernel(T** inputs_data, const int fixed_in_col, - const int out_rows, const int out_cols, - T* output_data) { +__device__ void ConcatKernelDetail(const T** inputs_data, + const int fixed_in_col, const int out_rows, + const int out_cols, T* output_data) { int tid_x = blockIdx.x * blockDim.x + threadIdx.x; for (; tid_x < out_cols; tid_x += blockDim.x * gridDim.x) { int split = tid_x * 1.0 / fixed_in_col; int in_offset = tid_x - split * fixed_in_col; - T* input_ptr = inputs_data[split]; + const T* input_ptr = inputs_data[split]; int tid_y = blockIdx.y * blockDim.y + threadIdx.y; for (; tid_y < out_rows; tid_y += blockDim.y * gridDim.y) { output_data[tid_y * out_cols + tid_x] = @@ -66,6 +66,52 @@ __global__ void ConcatKernel(T** inputs_data, const int fixed_in_col, } } +template +__global__ void ConcatKernel(const T* input_addr0, const T* input_addr1, + const int fixed_in_col, const int out_rows, + const int out_cols, T* output_data) { + const T* inputs_data[2]; + inputs_data[0] = input_addr0; + inputs_data[1] = input_addr1; + ConcatKernelDetail(inputs_data, fixed_in_col, out_rows, out_cols, + output_data); +} + +template +__global__ void ConcatKernel(const T* input_addr0, const T* input_addr1, + const T* input_addr2, const int fixed_in_col, + const int out_rows, const int out_cols, + T* output_data) { + const T* inputs_data[3]; + inputs_data[0] = input_addr0; + inputs_data[1] = input_addr1; + inputs_data[2] = input_addr2; + ConcatKernelDetail(inputs_data, fixed_in_col, out_rows, out_cols, + output_data); +} + +template +__global__ void ConcatKernel(const T* input_addr0, const T* input_addr1, + const T* input_addr2, const T* input_addr3, + const int fixed_in_col, const int out_rows, + const int out_cols, T* output_data) { + const T* inputs_data[4]; + inputs_data[0] = input_addr0; + inputs_data[1] = input_addr1; + inputs_data[2] = input_addr2; + inputs_data[3] = input_addr3; + ConcatKernelDetail(inputs_data, fixed_in_col, out_rows, out_cols, + output_data); +} + +template +__global__ void ConcatKernel(const T** inputs_data, const int in_num, + const int fixed_in_col, const int out_rows, + const int out_cols, T* output_data) { + ConcatKernelDetail(inputs_data, fixed_in_col, out_rows, out_cols, + output_data); +} + template __global__ void SplitKernel(const T* input_data, const int in_row, const int in_col, const int* out_cols, @@ -94,9 +140,9 @@ __global__ void SplitKernel(const T* input_data, const int in_row, } template -__global__ void SplitKernel(const T* input_data, const int in_row, - const int in_col, const int fixed_out_col, - T** outputs_data) { +__device__ void SplitKernelDetail(const T* input_data, const int in_row, + const int in_col, const int fixed_out_col, + T** outputs_data) { int tid_x = blockIdx.x * blockDim.x + threadIdx.x; for (; tid_x < in_col; tid_x += blockDim.x * gridDim.x) { int split = tid_x / fixed_out_col; @@ -111,6 +157,70 @@ __global__ void SplitKernel(const T* input_data, const int in_row, } } +template +__global__ void SplitKernel(const T* input_data, const int in_row, + const int in_col, const int fixed_out_col, + T** outputs_data) { + SplitKernelDetail(input_data, in_row, in_col, fixed_out_col, outputs_data); +} + +template +__global__ void SplitKernel(const T* input_data, const int in_row, + const int in_col, const int fixed_out_col, + T* outputs_addr0, T* outputs_addr1) { + T* outputs_data[2]; + outputs_data[0] = outputs_addr0; + outputs_data[1] = outputs_addr1; + SplitKernelDetail(input_data, in_row, in_col, fixed_out_col, outputs_data); +} + +template +__global__ void SplitKernel(const T* input_data, const int in_row, + const int in_col, const int fixed_out_col, + T* outputs_addr0, T* outputs_addr1, + T* outputs_addr2) { + T* outputs_data[3]; + outputs_data[0] = outputs_addr0; + outputs_data[1] = outputs_addr1; + outputs_data[2] = outputs_addr2; + SplitKernelDetail(input_data, in_row, in_col, fixed_out_col, outputs_data); +} + +template +__global__ void SplitKernel(const T* input_data, const int in_row, + const int in_col, const int fixed_out_col, + T* outputs_addr0, T* outputs_addr1, + T* outputs_addr2, T* outputs_addr3) { + T* outputs_data[4]; + outputs_data[0] = outputs_addr0; + outputs_data[1] = outputs_addr1; + outputs_data[2] = outputs_addr2; + outputs_data[3] = outputs_addr3; + SplitKernelDetail(input_data, in_row, in_col, fixed_out_col, outputs_data); +} + +static inline void GetBlockDims(const platform::CUDADeviceContext& context, + int num_rows, int num_cols, dim3* block_dims, + dim3* grid_dims) { + // Set the thread block and grid according to CurrentDeviceId + const int kThreadsPerBlock = 1024; + int block_cols = kThreadsPerBlock; + if (num_cols < kThreadsPerBlock) { // block_cols is aligned by 32. + block_cols = ((num_cols + 31) >> 5) << 5; + } + int block_rows = kThreadsPerBlock / block_cols; + *block_dims = dim3(block_cols, block_rows, 1); + + int max_threads = context.GetMaxPhysicalThreadCount(); + int max_blocks = std::max(max_threads / kThreadsPerBlock, 1); + + int grid_cols = + std::min((num_cols + block_cols - 1) / block_cols, max_blocks); + int grid_rows = + std::min(max_blocks / grid_cols, std::max(num_rows / block_rows, 1)); + *grid_dims = dim3(grid_cols, grid_rows, 1); +} + /* * All tensors' dimension should be the same and the values of * each dimension must be the same, except the axis dimension. @@ -131,53 +241,55 @@ class ConcatFunctor { int in_col = input[0].numel() / in_row; int out_row = in_row, out_col = 0; - std::vector inputs_data; + std::vector inputs_data(in_num); std::vector inputs_col(in_num + 1); - inputs_data.reserve(in_num); inputs_col[0] = 0; - bool sameShape = true; + bool has_same_shape = true; for (int i = 0; i < in_num; ++i) { int t_cols = input[i].numel() / in_row; - if (sameShape) { - if (t_cols != in_col) sameShape = false; + if (has_same_shape) { + if (t_cols != in_col) has_same_shape = false; } out_col += t_cols; inputs_col[i + 1] = out_col; - inputs_data.emplace_back(input[i].data()); + inputs_data[i] = input[i].data(); } - // computation - // set the thread block and grid according to CurrentDeviceId - const int kThreadsPerBlock = 1024; - int block_cols = kThreadsPerBlock; - if (out_col < kThreadsPerBlock) { // block_cols is aligned by 32. - block_cols = ((out_col + 31) >> 5) << 5; + dim3 block_dims; + dim3 grid_dims; + GetBlockDims(context, out_row, out_col, &block_dims, &grid_dims); + + memory::allocation::AllocationPtr tmp_dev_ins_data; + const T** dev_ins_data = nullptr; + if (!has_same_shape || in_num < 2 || in_num > 4) { + tmp_dev_ins_data = + platform::DeviceTemporaryAllocator::Instance().Get(context).Allocate( + inputs_data.size() * sizeof(T*)); + memory::Copy(boost::get(context.GetPlace()), + tmp_dev_ins_data->ptr(), platform::CPUPlace(), + static_cast(inputs_data.data()), + inputs_data.size() * sizeof(T*), context.stream()); + dev_ins_data = reinterpret_cast(tmp_dev_ins_data->ptr()); } - int block_rows = kThreadsPerBlock / block_cols; - dim3 block_size = dim3(block_cols, block_rows, 1); - - int max_threads = context.GetMaxPhysicalThreadCount(); - int max_blocks = std::max(max_threads / kThreadsPerBlock, 1); - - int grid_cols = - std::min((out_col + block_cols - 1) / block_cols, max_blocks); - int grid_rows = - std::min(max_blocks / grid_cols, std::max(out_row / block_rows, 1)); - dim3 grid_size = dim3(grid_cols, grid_rows, 1); - - auto tmp_dev_ins_data = - platform::DeviceTemporaryAllocator::Instance().Get(context).Allocate( - inputs_data.size() * sizeof(T*)); - memory::Copy(boost::get(context.GetPlace()), - tmp_dev_ins_data->ptr(), platform::CPUPlace(), - static_cast(inputs_data.data()), - inputs_data.size() * sizeof(T*), context.stream()); - T** dev_ins_data = reinterpret_cast(tmp_dev_ins_data->ptr()); - - if (sameShape) { - ConcatKernel<<>>( - dev_ins_data, in_col, out_row, out_col, output->data()); + + if (has_same_shape) { + if (in_num == 2) { + ConcatKernel<<>>( + inputs_data[0], inputs_data[1], in_col, out_row, out_col, + output->data()); + } else if (in_num == 3) { + ConcatKernel<<>>( + inputs_data[0], inputs_data[1], inputs_data[2], in_col, out_row, + out_col, output->data()); + } else if (in_num == 4) { + ConcatKernel<<>>( + inputs_data[0], inputs_data[1], inputs_data[2], inputs_data[3], + in_col, out_row, out_col, output->data()); + } else { + ConcatKernel<<>>( + dev_ins_data, in_num, in_col, out_row, out_col, output->data()); + } } else { auto tmp_dev_ins_col_data = platform::DeviceTemporaryAllocator::Instance().Get(context).Allocate( @@ -188,7 +300,7 @@ class ConcatFunctor { inputs_col.size() * sizeof(int), context.stream()); int* dev_ins_col_data = static_cast(tmp_dev_ins_col_data->ptr()); - ConcatKernel<<>>( + ConcatKernel<<>>( dev_ins_data, dev_ins_col_data, static_cast(inputs_col.size()), out_row, out_col, output->data()); } @@ -216,7 +328,7 @@ class SplitFunctor { int out0_col = ref_inputs[0]->numel() / out_row; int in_col = 0, in_row = out_row; - bool sameShape = true; + bool has_same_shape = true; std::vector outputs_data(o_num); std::vector outputs_cols(o_num + 1); @@ -224,8 +336,8 @@ class SplitFunctor { outputs_cols[0] = 0; for (int i = 0; i < o_num; ++i) { int t_col = ref_inputs.at(i)->numel() / out_row; - if (sameShape) { - if (t_col != out0_col) sameShape = false; + if (has_same_shape) { + if (t_col != out0_col) has_same_shape = false; } in_col += t_col; outputs_cols[i + 1] = in_col; @@ -236,36 +348,40 @@ class SplitFunctor { } } - // computation - const int kThreadsPerBlock = 1024; - int block_cols = kThreadsPerBlock; - if (in_col < kThreadsPerBlock) { // block_cols is aligned by 32. - block_cols = ((in_col + 31) >> 5) << 5; + dim3 block_dims; + dim3 grid_dims; + GetBlockDims(context, out_row, in_col, &block_dims, &grid_dims); + + memory::allocation::AllocationPtr tmp_dev_outs_data; + T** dev_out_gpu_data = nullptr; + if (!has_same_shape || o_num < 2 || o_num > 4) { + tmp_dev_outs_data = + platform::DeviceTemporaryAllocator::Instance().Get(context).Allocate( + outputs_data.size() * sizeof(T*)); + memory::Copy(boost::get(context.GetPlace()), + tmp_dev_outs_data->ptr(), platform::CPUPlace(), + reinterpret_cast(outputs_data.data()), + outputs_data.size() * sizeof(T*), context.stream()); + dev_out_gpu_data = reinterpret_cast(tmp_dev_outs_data->ptr()); } - int block_rows = kThreadsPerBlock / block_cols; - dim3 block_size = dim3(block_cols, block_rows, 1); - - int max_threads = context.GetMaxPhysicalThreadCount(); - int max_blocks = std::max(max_threads / kThreadsPerBlock, 1); - - int grid_cols = - std::min((in_col + block_cols - 1) / block_cols, max_blocks); - int grid_rows = - std::min(max_blocks / grid_cols, std::max(out_row / block_rows, 1)); - dim3 grid_size = dim3(grid_cols, grid_rows, 1); - - auto tmp_dev_outs_data = - platform::DeviceTemporaryAllocator::Instance().Get(context).Allocate( - outputs_data.size() * sizeof(T*)); - memory::Copy(boost::get(context.GetPlace()), - tmp_dev_outs_data->ptr(), platform::CPUPlace(), - reinterpret_cast(outputs_data.data()), - outputs_data.size() * sizeof(T*), context.stream()); - T** dev_out_gpu_data = reinterpret_cast(tmp_dev_outs_data->ptr()); - - if (sameShape) { - SplitKernel<<>>( - input.data(), in_row, in_col, out0_col, dev_out_gpu_data); + + if (has_same_shape) { + if (o_num == 2) { + SplitKernel<<>>( + input.data(), in_row, in_col, out0_col, outputs_data[0], + outputs_data[1]); + } else if (o_num == 3) { + SplitKernel<<>>( + input.data(), in_row, in_col, out0_col, outputs_data[0], + outputs_data[1], outputs_data[2]); + } else if (o_num == 4) { + SplitKernel<<>>( + input.data(), in_row, in_col, out0_col, outputs_data[0], + outputs_data[1], outputs_data[2], outputs_data[3]); + } else { + SplitKernel<<>>( + input.data(), in_row, in_col, out0_col, dev_out_gpu_data); + } } else { auto tmp_dev_ins_col_data = platform::DeviceTemporaryAllocator::Instance().Get(context).Allocate( @@ -277,7 +393,7 @@ class SplitFunctor { int* dev_outs_col_data = reinterpret_cast(tmp_dev_ins_col_data->ptr()); - SplitKernel<<>>( + SplitKernel<<>>( input.data(), in_row, in_col, dev_outs_col_data, static_cast(outputs_cols.size()), dev_out_gpu_data); } diff --git a/paddle/fluid/operators/math/concat_test.cc b/paddle/fluid/operators/math/concat_test.cc index 8ba9e8e8ec1344edc3beaf7f4a58f99107cc0e9c..411dbca25bb48c99dfd16779f54e46a3e80d0d4e 100644 --- a/paddle/fluid/operators/math/concat_test.cc +++ b/paddle/fluid/operators/math/concat_test.cc @@ -17,26 +17,24 @@ limitations under the License. */ #include "paddle/fluid/framework/tensor_util.h" #include "paddle/fluid/operators/math/concat_and_split.h" +/** + * case 1: + * inputs: + * t_a.shape: [2, 3, 4] + * t_b.shape: [3, 3, 4] + * output: + * out.shape: [5, 3, 4] + */ template -void testConcat() { +void ConcatCase1(DeviceContext* context) { paddle::framework::Tensor input_a_cpu; paddle::framework::Tensor input_b_cpu; paddle::framework::Tensor out_cpu; + paddle::framework::Tensor input_a; paddle::framework::Tensor input_b; paddle::framework::Tensor out; - DeviceContext* context = new DeviceContext(Place()); - // DeviceContext context(Place()); - - /** - * cast1: - * inputs: - * t_a.shape: [2, 3, 4] - * t_b.shape: [3, 3, 4] - * output: - * out.shape: [5, 3, 4] - */ auto dim_a = paddle::framework::make_ddim({2, 3, 4}); auto dim_b = paddle::framework::make_ddim({3, 3, 4}); auto dim_out = paddle::framework::make_ddim({5, 3, 4}); @@ -51,8 +49,8 @@ void testConcat() { out_cpu.mutable_data(dim_out, paddle::platform::CPUPlace()); } - int* a_ptr; - int* b_ptr; + int* a_ptr = nullptr; + int* b_ptr = nullptr; if (paddle::platform::is_gpu_place(Place())) { a_ptr = input_a_cpu.data(); b_ptr = input_b_cpu.data(); @@ -84,7 +82,7 @@ void testConcat() { PADDLE_ENFORCE_EQ(input_a.dims(), dim_a); PADDLE_ENFORCE_EQ(input_b.dims(), dim_b); - int* out_ptr; + int* out_ptr = nullptr; if (paddle::platform::is_gpu_place(Place())) { paddle::framework::TensorCopySync(out, paddle::platform::CPUPlace(), &out_cpu); @@ -104,28 +102,42 @@ void testConcat() { ++idx_a; } } - // - /** - * cast2: - * inputs: - * t_a.shape: [2, 3, 4] - * t_b.shape: [2, 4, 4] - * output: - * out.shape: [2, 7, 4] - */ - dim_a = paddle::framework::make_ddim({2, 3, 4}); - dim_b = paddle::framework::make_ddim({2, 4, 4}); - dim_out = paddle::framework::make_ddim({2, 7, 4}); - - input_a.Resize(dim_a); - input_b.Resize(dim_b); - out.Resize(dim_out); +} + +/** + * case 2: + * inputs: + * t_a.shape: [2, 3, 4] + * t_b.shape: [2, 4, 4] + * output: + * out.shape: [2, 7, 4] + */ +template +void ConcatCase2(DeviceContext* context) { + paddle::framework::Tensor input_a_cpu; + paddle::framework::Tensor input_b_cpu; + paddle::framework::Tensor out_cpu; + + paddle::framework::Tensor input_a; + paddle::framework::Tensor input_b; + paddle::framework::Tensor out; + + auto dim_a = paddle::framework::make_ddim({2, 3, 4}); + auto dim_b = paddle::framework::make_ddim({2, 4, 4}); + auto dim_out = paddle::framework::make_ddim({2, 7, 4}); + + input_a.mutable_data(dim_a, Place()); + input_b.mutable_data(dim_b, Place()); + out.mutable_data(dim_out, Place()); + if (paddle::platform::is_gpu_place(Place())) { - input_a_cpu.Resize(dim_a); - input_b_cpu.Resize(dim_b); - out_cpu.Resize(dim_out); + input_a_cpu.mutable_data(dim_a, paddle::platform::CPUPlace()); + input_b_cpu.mutable_data(dim_b, paddle::platform::CPUPlace()); + out_cpu.mutable_data(dim_out, paddle::platform::CPUPlace()); } + int* a_ptr = nullptr; + int* b_ptr = nullptr; if (paddle::platform::is_gpu_place(Place())) { a_ptr = input_a_cpu.data(); b_ptr = input_b_cpu.data(); @@ -146,16 +158,18 @@ void testConcat() { paddle::framework::TensorCopySync(input_b_cpu, Place(), &input_b); } - input.clear(); + std::vector input; input.push_back(input_a); input.push_back(input_b); + paddle::operators::math::ConcatFunctor concat_functor; concat_functor(*context, input, 1, &out); // check the dim of input_a, input_b PADDLE_ENFORCE_EQ(input_a.dims(), dim_a); PADDLE_ENFORCE_EQ(input_b.dims(), dim_b); + int* out_ptr = nullptr; if (paddle::platform::is_gpu_place(Place())) { paddle::framework::TensorCopySync(out, paddle::platform::CPUPlace(), &out_cpu); @@ -164,8 +178,8 @@ void testConcat() { out_ptr = out.data(); } - cols = 3 * 4; - idx_a = 0, idx_b = 0; + int cols = 3 * 4; + int idx_a = 0, idx_b = 0; for (int i = 0; i < 2; ++i) { for (int j = 0; j < 28; ++j) { if (j >= cols) { @@ -177,28 +191,42 @@ void testConcat() { } } } +} + +/** + * case 3: + * inputs: + * t_a.shape: [2, 3, 5] + * t_b.shape: [2, 3, 4] + * output: + * out.shape: [2, 3, 9] + */ +template +void ConcatCase3(DeviceContext* context) { + paddle::framework::Tensor input_a_cpu; + paddle::framework::Tensor input_b_cpu; + paddle::framework::Tensor out_cpu; + + paddle::framework::Tensor input_a; + paddle::framework::Tensor input_b; + paddle::framework::Tensor out; + + auto dim_a = paddle::framework::make_ddim({2, 3, 4}); + auto dim_b = paddle::framework::make_ddim({2, 3, 5}); + auto dim_out = paddle::framework::make_ddim({2, 3, 9}); + + input_a.mutable_data(dim_a, Place()); + input_b.mutable_data(dim_b, Place()); + out.mutable_data(dim_out, Place()); - /** - * cast3: - * inputs: - * t_a.shape: [2, 3, 5] - * t_b.shape: [2, 3, 4] - * output: - * out.shape: [2, 3, 9] - */ - dim_a = paddle::framework::make_ddim({2, 3, 4}); - dim_b = paddle::framework::make_ddim({2, 3, 5}); - dim_out = paddle::framework::make_ddim({2, 3, 9}); - - input_a.Resize(dim_a); - input_b.Resize(dim_b); - out.Resize(dim_out); if (paddle::platform::is_gpu_place(Place())) { - input_a_cpu.Resize(dim_a); - input_b_cpu.Resize(dim_b); - out_cpu.Resize(dim_out); + input_a_cpu.mutable_data(dim_a, paddle::platform::CPUPlace()); + input_b_cpu.mutable_data(dim_b, paddle::platform::CPUPlace()); + out_cpu.mutable_data(dim_out, paddle::platform::CPUPlace()); } + int* a_ptr = nullptr; + int* b_ptr = nullptr; if (paddle::platform::is_gpu_place(Place())) { a_ptr = input_a_cpu.data(); b_ptr = input_b_cpu.data(); @@ -219,16 +247,18 @@ void testConcat() { paddle::framework::TensorCopySync(input_b_cpu, Place(), &input_b); } - input.clear(); + std::vector input; input.push_back(input_a); input.push_back(input_b); + paddle::operators::math::ConcatFunctor concat_functor; concat_functor(*context, input, 2, &out); // check the dim of input_a, input_b PADDLE_ENFORCE_EQ(input_a.dims(), dim_a); PADDLE_ENFORCE_EQ(input_b.dims(), dim_b); + int* out_ptr = nullptr; if (paddle::platform::is_gpu_place(Place())) { paddle::framework::TensorCopySync(out, paddle::platform::CPUPlace(), &out_cpu); @@ -238,8 +268,8 @@ void testConcat() { } // check the data - cols = 4; - idx_a = 0, idx_b = 0; + int cols = 4; + int idx_a = 0, idx_b = 0; for (int i = 0; i < 6; ++i) { for (int j = 0; j < 9; ++j) { if (j >= cols) { @@ -251,29 +281,43 @@ void testConcat() { } } } +} + +/** + * case 4: + * inputs: + * axis = 1 + * t_a.shape: [2, 3, 4] + * t_b.shape: [2, 3, 4] + * output: + * out.shape: [2, 6, 4] + */ +template +void ConcatCase4(DeviceContext* context) { + paddle::framework::Tensor input_a_cpu; + paddle::framework::Tensor input_b_cpu; + paddle::framework::Tensor out_cpu; + + paddle::framework::Tensor input_a; + paddle::framework::Tensor input_b; + paddle::framework::Tensor out; + + auto dim_a = paddle::framework::make_ddim({2, 3, 4}); + auto dim_b = paddle::framework::make_ddim({2, 3, 4}); + auto dim_out = paddle::framework::make_ddim({2, 6, 4}); + + input_a.mutable_data(dim_a, Place()); + input_b.mutable_data(dim_b, Place()); + out.mutable_data(dim_out, Place()); - /** - * cast4: - * inputs: - * axis = 1 - * t_a.shape: [2, 3, 4] - * t_b.shape: [2, 3, 4] - * output: - * out.shape: [2, 6, 4] - */ - dim_a = paddle::framework::make_ddim({2, 3, 4}); - dim_b = paddle::framework::make_ddim({2, 3, 4}); - dim_out = paddle::framework::make_ddim({2, 6, 4}); - - input_a.Resize(dim_a); - input_b.Resize(dim_b); - out.Resize(dim_out); if (paddle::platform::is_gpu_place(Place())) { - input_a_cpu.Resize(dim_a); - input_b_cpu.Resize(dim_b); - out_cpu.Resize(dim_out); + input_a_cpu.mutable_data(dim_a, paddle::platform::CPUPlace()); + input_b_cpu.mutable_data(dim_b, paddle::platform::CPUPlace()); + out_cpu.mutable_data(dim_out, paddle::platform::CPUPlace()); } + int* a_ptr = nullptr; + int* b_ptr = nullptr; if (paddle::platform::is_gpu_place(Place())) { a_ptr = input_a_cpu.data(); b_ptr = input_b_cpu.data(); @@ -294,16 +338,19 @@ void testConcat() { paddle::framework::TensorCopySync(input_b_cpu, Place(), &input_b); } - input.clear(); + std::vector input; input.push_back(input_a); input.push_back(input_b); + paddle::operators::math::ConcatFunctor concat_functor; concat_functor(*context, input, 1, &out); + context->Wait(); // check the dim of input_a, input_b PADDLE_ENFORCE_EQ(input_a.dims(), dim_a); PADDLE_ENFORCE_EQ(input_b.dims(), dim_b); + int* out_ptr = nullptr; if (paddle::platform::is_gpu_place(Place())) { paddle::framework::TensorCopySync(out, paddle::platform::CPUPlace(), &out_cpu); @@ -313,8 +360,8 @@ void testConcat() { } // check the data - cols = 12; - idx_a = 0, idx_b = 0; + int cols = 12; + int idx_a = 0, idx_b = 0; for (int i = 0; i < 2; ++i) { for (int j = 0; j < 24; ++j) { if (j >= cols) { @@ -328,10 +375,21 @@ void testConcat() { } } +template +void TestConcatMain() { + DeviceContext* context = new DeviceContext(Place()); + + ConcatCase1(context); + ConcatCase2(context); + ConcatCase3(context); + ConcatCase4(context); +} + TEST(math, concat) { - testConcat(); + TestConcatMain(); #ifdef PADDLE_WITH_CUDA - testConcat(); + TestConcatMain(); #endif } diff --git a/paddle/fluid/operators/math/context_project.h b/paddle/fluid/operators/math/context_project.h index f6094369567cd0481b68ebbad46d4a3717eb6ead..e9019c6d2fe6890ee92cb5a3b047666e3c2a7e04 100644 --- a/paddle/fluid/operators/math/context_project.h +++ b/paddle/fluid/operators/math/context_project.h @@ -144,7 +144,8 @@ class ContextProjectFunctor { sequence_height = static_cast(out_t.dims()[0]); // add up trainable data - out_t.Resize({sequence_height * context_length, sequence_width}); + out_t.Resize({static_cast(sequence_height) * context_length, + sequence_width}); if (up_pad > 0) { // add up pad int padding_rows = std::min( @@ -191,7 +192,8 @@ class ContextProjectFunctor { &out_t_sub); } } - out_t.Resize({sequence_height, context_length * sequence_width}); + out_t.Resize({sequence_height, + static_cast(context_length) * sequence_width}); } } } @@ -260,7 +262,8 @@ class ContextProjectGradFunctor { static_cast(lod_level_0[i + 1])); sequence_height = static_cast(out_t.dims()[0]); - out_t.Resize({sequence_height * context_length, sequence_width}); + out_t.Resize({static_cast(sequence_height) * context_length, + sequence_width}); if (up_pad > 0) { int padding_rows = std::min( @@ -308,7 +311,8 @@ class ContextProjectGradFunctor { w_sub.data()); } } - out_t.Resize({sequence_height, context_length * sequence_width}); + out_t.Resize({sequence_height, + static_cast(context_length) * sequence_width}); } } } diff --git a/paddle/fluid/operators/math/cpu_vec.h b/paddle/fluid/operators/math/cpu_vec.h index 57726956cfba802183903b436c82b15c34d8fcc9..4406a5587188eabb6933175010b4f053dbf6c661 100644 --- a/paddle/fluid/operators/math/cpu_vec.h +++ b/paddle/fluid/operators/math/cpu_vec.h @@ -54,7 +54,14 @@ inline void vec_scal(const int n, const T a, T* x) { #ifdef PADDLE_WITH_MKLML template <> inline void vec_exp(const int n, const float* x, float* y) { - platform::dynload::vsExp(n, x, y); + constexpr int small_enough = 128; + if (n < small_enough) { + for (int i = 0; i < n; ++i) { + y[i] = std::exp(x[i]); + } + } else { + platform::dynload::vsExp(n, x, y); + } } template <> @@ -128,6 +135,120 @@ inline void vec_scal(const int n, const float a, vec_scal(n, a, x, y); } +template +inline void vec_sum(const size_t n, const T* x, T* s) { + s[0] = x[0]; + for (size_t i = 1; i < n; ++i) { + s[0] += x[i]; + } +} + +template <> +inline void vec_sum(const size_t n, const float* x, + float* s) { +#ifdef __AVX__ + constexpr unsigned int block = YMM_FLOAT_BLOCK; + if (n < block) { + vec_sum(n, x, s); + return; + } + + unsigned int i, end; + i = end = 0; + s[0] = 0.f; + + end = n & ~(block - 1); + __m256 tmp = _mm256_setzero_ps(); + for (i = 0; i < end; i += block) { + tmp = _mm256_add_ps(tmp, _mm256_load_ps(x + i)); + } + + __m256 hsum = _mm256_hadd_ps(tmp, tmp); + hsum = _mm256_add_ps(hsum, _mm256_permute2f128_ps(hsum, hsum, 0x1)); + _mm_store_ss(s, _mm_hadd_ps(_mm256_castps256_ps128(hsum), + _mm256_castps256_ps128(hsum))); + + for (; i < n; i++) { + s[0] += x[i]; + } +#else + vec_sum(n, x, s); +#endif +} + +template +inline void vec_mul(const size_t n, const T* x, const T* y, T* z) { + for (size_t i = 0; i < n; ++i) { + z[i] = x[i] * y[i]; + } +} + +template <> +inline void vec_mul(const size_t n, const float* x, + const float* y, float* z) { +#ifdef __AVX__ + constexpr unsigned int block = YMM_FLOAT_BLOCK; + if (n < block) { + vec_mul(n, x, y, z); + return; + } + + unsigned int i = 0, end = 0; + end = n & ~(block - 1); + for (i = 0; i < end; i += block) { + _mm256_storeu_ps( + z + i, _mm256_mul_ps(_mm256_loadu_ps(x + i), _mm256_loadu_ps(y + i))); + } + + for (; i < n; i++) { + z[i] = x[i] * y[i]; + } +#else + vec_mul(n, x, y, z); +#endif +} + +template +inline void vec_mul_reduce(const size_t n, const T* x, const T* y, T* z) { + z[0] = x[0] * y[0]; + for (size_t i = 1; i < n; ++i) { + z[0] += x[i] * y[i]; + } +} + +template <> +inline void vec_mul_reduce(const size_t n, const float* x, + const float* y, float* z) { +#ifdef __AVX__ + constexpr unsigned int block = YMM_FLOAT_BLOCK; + if (n < block) { + vec_mul_reduce(n, x, y, z); + return; + } + + unsigned int i = 0, end = 0; + z[0] = 0.f; + + end = n & ~(block - 1); + __m256 tmp = _mm256_setzero_ps(); + for (i = 0; i < end; i += block) { + tmp = _mm256_add_ps( + tmp, _mm256_mul_ps(_mm256_loadu_ps(x + i), _mm256_loadu_ps(y + i))); + } + + __m256 hsum = _mm256_hadd_ps(tmp, tmp); + hsum = _mm256_add_ps(hsum, _mm256_permute2f128_ps(hsum, hsum, 0x1)); + _mm_store_ss(z, _mm_hadd_ps(_mm256_castps256_ps128(hsum), + _mm256_castps256_ps128(hsum))); + + for (; i < n; i++) { + z[0] += x[i] * y[i]; + } +#else + vec_mul_reduce(n, x, y, z); +#endif +} + template inline void vec_bias_sub(const int n, const T a, const T* x, T* y) { for (int i = 0; i < n; ++i) { @@ -242,6 +363,39 @@ inline void vec_cross(const int n, const float* x, vec_cross(n, x, y, z, out); } +template +inline void vec_clip(const size_t n, const T a, const T* x, T* y) { + for (size_t i = 0; i < n; ++i) { + y[i] = x[i] < a ? a : x[i]; + } +} + +template <> +inline void vec_clip(const size_t n, const float a, + const float* x, float* y) { +#ifdef __AVX__ + constexpr unsigned int block = YMM_FLOAT_BLOCK; + if (n < block) { + vec_clip(n, a, x, y); + return; + } + + unsigned int i = 0, end = 0; + end = n & ~(block - 1); + __m256 threshold = _mm256_set1_ps(a); + + for (i = 0; i < end; i += block) { + _mm256_storeu_ps(y + i, _mm256_max_ps(_mm256_loadu_ps(x + i), threshold)); + } + + for (; i < n; i++) { + y[i] = x[i] < a ? a : x[i]; + } +#else + vec_clip(n, a, x, y); +#endif +} + template inline void vec_add_bias(const int n, const T a, const T* x, T* y) { for (int i = 0; i < n; ++i) { diff --git a/paddle/fluid/operators/math/cpu_vec_test.cc b/paddle/fluid/operators/math/cpu_vec_test.cc index 28eb9cadc9d4258bf4f8f71a06e029531e448014..f2f80f836fdce21e4f41ef11472805253cd6ec57 100644 --- a/paddle/fluid/operators/math/cpu_vec_test.cc +++ b/paddle/fluid/operators/math/cpu_vec_test.cc @@ -65,12 +65,11 @@ void ref_relu(const int n, const T* x, T* y) { } template -void RandomVec(const int n, T* a) { +void RandomVec(const int n, T* a, const T lower = static_cast(-20.f), + const T upper = static_cast(20.f)) { static unsigned int seed = 100; std::mt19937 rng(seed++); std::uniform_real_distribution uniform_dist(0, 1); - const T lower = static_cast(-20.f); - const T upper = static_cast(20.f); for (int i = 0; i < n; ++i) { a[i] = static_cast(uniform_dist(rng) * (upper - lower) + lower); } @@ -144,6 +143,126 @@ TEST(CpuVecTest, relu) { TestAndBench(30, vec_relu, ref_relu); } +template +void compare_sum(size_t n, std::function tgt, + std::function ref) { + std::vector x(n); + T ytgt_data, yref_data; + RandomVec(n, x.data(), static_cast(-2), static_cast(2)); + + const T* x_data = x.data(); + tgt(n, x_data, &ytgt_data); + ref(n, x_data, &yref_data); + EXPECT_NEAR(ytgt_data, yref_data, 1e-3); +} + +TEST(CpuVecTest, vec_sum) { + namespace platform = paddle::platform; + using namespace paddle::operators::math; // NOLINT + for (size_t sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) { + compare_sum(sz, vec_sum, vec_sum); + compare_sum(sz, vec_sum, + vec_sum); + } + compare_sum(30U, vec_sum, vec_sum); +} + +template +void compare_clip( + size_t n, T threshold, + std::function tgt, + std::function ref) { + std::vector x(n); + std::vector ytgt(n), yref(n); + RandomVec(n, x.data(), static_cast(-2), static_cast(2)); + + const T* x_data = x.data(); + T* yref_data = yref.data(); + T* ytgt_data = ytgt.data(); + tgt(n, threshold, x_data, ytgt_data); + ref(n, threshold, x_data, yref_data); + for (int i = 0; i < n; ++i) { + EXPECT_NEAR(ytgt_data[i], yref_data[i], 1e-3); + } +} + +TEST(CpuVecTest, vec_clip) { + namespace platform = paddle::platform; + using namespace paddle::operators::math; // NOLINT + for (size_t sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) { + compare_clip(sz, -4.f, vec_clip, + vec_clip); + compare_clip(sz, -1.1f, vec_clip, + vec_clip); + } + compare_clip(30U, 1.0, vec_clip, + vec_clip); +} + +template +void compare_mul( + size_t n, std::function tgt, + std::function ref) { + std::vector x(n), y(n); + std::vector ztgt(n), zref(n); + + RandomVec(n, x.data(), static_cast(-2), static_cast(2)); + RandomVec(n, y.data(), static_cast(-2), static_cast(2)); + + const T* x_data = x.data(); + const T* y_data = y.data(); + T* ztgt_data = ztgt.data(); + T* zref_data = zref.data(); + + tgt(n, x_data, y_data, ztgt_data); + ref(n, x_data, y_data, zref_data); + for (size_t i = 0; i < n; ++i) { + EXPECT_NEAR(ztgt_data[i], zref_data[i], 1e-3); + } +} + +TEST(CpuVecTest, vec_mul) { + namespace platform = paddle::platform; + using namespace paddle::operators::math; // NOLINT + for (size_t sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) { + compare_mul(sz, vec_mul, vec_mul); + compare_mul(sz, vec_mul, + vec_mul); + } + compare_mul(30U, vec_mul, vec_mul); +} + +template +void compare_mul_reduce( + size_t n, std::function tgt, + std::function ref) { + std::vector x(n), y(n); + T ztgt_data, zref_data; + + RandomVec(n, x.data(), static_cast(-2), static_cast(2)); + RandomVec(n, y.data(), static_cast(-2), static_cast(2)); + + const T* x_data = x.data(); + const T* y_data = y.data(); + + tgt(n, x_data, y_data, &ztgt_data); + ref(n, x_data, y_data, &zref_data); + EXPECT_NEAR(ztgt_data, zref_data, 1e-3); +} + +TEST(CpuVecTest, vec_mul_reduce) { + namespace platform = paddle::platform; + using namespace paddle::operators::math; // NOLINT + for (size_t sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) { + compare_mul_reduce(sz, vec_mul_reduce, + vec_mul_reduce); + compare_mul_reduce(sz, vec_mul_reduce, + vec_mul_reduce); + } + compare_mul_reduce(30U, vec_mul_reduce, + vec_mul_reduce); +} + template void TestInplace(const int n, std::function tgt, std::function ref) { diff --git a/paddle/fluid/operators/math/functors.h b/paddle/fluid/operators/math/functors.h index 955c0b6bad5f81e64e2e6d1f7521abec49d57721..e98bf82169aae0f541e51a6a30f02f602272bb34 100644 --- a/paddle/fluid/operators/math/functors.h +++ b/paddle/fluid/operators/math/functors.h @@ -78,6 +78,48 @@ struct ReluGradFunctor { inline HOSTDEVICE T UseXAndOut(T x, T out) { return out > 0 ? 1 : 0; } }; +template +struct TanhFunctor { + const T kMin = static_cast(-40); + const T kMax = static_cast(13); + inline HOSTDEVICE T operator()(T x) { + // y = 2 / (1 + e^-2x) - 1 + T t0 = 2 * x; + T t1 = (t0 < kMin) ? kMin : ((t0 > kMax) ? kMax : t0); + return static_cast(2) / (static_cast(1) + std::exp(-t1)) - + static_cast(1); + } +}; + +template +struct TanhGradFunctor { + inline HOSTDEVICE T UseX(T x) { return static_cast(1) - x * x; } + inline HOSTDEVICE T UseOut(T out) { return static_cast(1) - out * out; } + inline HOSTDEVICE T UseXAndOut(T x, T out) { + return static_cast(1) - out * out; + } +}; + +template +struct SigmoidFunctor { + const T kMin = static_cast(-40); + const T kMax = static_cast(13); + inline HOSTDEVICE T operator()(T x) { + // y = 1 / (1 + e^-x) + T tmp = (x < kMin) ? kMin : ((x > kMax) ? kMax : x); + return static_cast(1) / (static_cast(1) + std::exp(-tmp)); + } +}; + +template +struct SigmoidGradFunctor { + inline HOSTDEVICE T UseX(T x) { return x * (static_cast(1) - x); } + inline HOSTDEVICE T UseOut(T out) { return out * (static_cast(1) - out); } + inline HOSTDEVICE T UseXAndOut(T x, T out) { + return out * (static_cast(1) - out); + } +}; + } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/math/gru_compute.cu b/paddle/fluid/operators/math/gru_compute.cu index 75417cced237c48dda1f6e87c0647b10a66d0907..b564f990b4920a3a01b6ce0dd53e8f5e5d0464aa 100644 --- a/paddle/fluid/operators/math/gru_compute.cu +++ b/paddle/fluid/operators/math/gru_compute.cu @@ -30,25 +30,31 @@ struct GRUUnitFunctor { dim3 threads; dim3 grid; if (batch_size == 1) { - constexpr int tiled_size = 16; - int frame_blocks = (frame_size * 2 + tiled_size - 1) / tiled_size; - threads = dim3(tiled_size, 1); - grid = dim3(frame_blocks, 1); - - detail::KeFastCollectiveGruGate<<>>( - value.gate_value, value.prev_out_value, value.gate_weight, - value.reset_output_value, frame_size, active_gate); - - frame_blocks = (frame_size + tiled_size - 1) / tiled_size; - grid = dim3(frame_blocks, 1); - detail::KeFastCollectiveGruOut<<>>( - value.state_weight, value.prev_out_value, value.output_value, - value.gate_value, value.reset_output_value, frame_size, active_node, - origin_mode); - - return; + if (context.GetComputeCapability() >= 70) { + constexpr int tiled_size = 16; + int frame_blocks = (frame_size * 2 + tiled_size - 1) / tiled_size; + threads = dim3(tiled_size, 1); + grid = dim3(frame_blocks, 1); + detail::KeFastCollectiveGruGate< + T, tiled_size><<>>( + value.gate_value, value.prev_out_value, value.gate_weight, + value.reset_output_value, frame_size, active_gate); + + frame_blocks = (frame_size + tiled_size - 1) / tiled_size; + grid = dim3(frame_blocks, 1); + detail::KeFastCollectiveGruOut< + T, tiled_size><<>>( + value.state_weight, value.prev_out_value, value.output_value, + value.gate_value, value.reset_output_value, frame_size, active_node, + origin_mode); + + return; + } else { + int frame_per_block = frame_size <= 1024 ? frame_size : 1024; + int frame_blocks = (frame_size + 1024 - 1) / 1024; + threads = dim3(frame_per_block, 1); + grid = dim3(frame_blocks, 1); + } } else { threads = dim3(32, 32); grid = dim3((frame_size + 32 - 1) / 32, (batch_size + 32 - 1) / 32); diff --git a/paddle/fluid/operators/math/sequence_padding.cc b/paddle/fluid/operators/math/sequence_padding.cc index 25f06a25a0638cbb394df58d35f88307941d117f..4630689dec160da145e607f662a802444ac98b55 100644 --- a/paddle/fluid/operators/math/sequence_padding.cc +++ b/paddle/fluid/operators/math/sequence_padding.cc @@ -59,6 +59,22 @@ void CopyValidData(framework::Tensor* dst_tensor, } } +template +static void fast_mem_init(void* dest, size_t dest_size, const T* src, + size_t num_bytes) { + if (dest == nullptr || dest_size == 0 || src == nullptr) return; + + memcpy(dest, src, num_bytes); + + dest_size *= num_bytes; + while (dest_size > num_bytes) { + size_t remaining = dest_size - num_bytes; + size_t count = (remaining > num_bytes) ? num_bytes : remaining; + memcpy((unsigned char*)dest + num_bytes, dest, count); + num_bytes += count; + } +} + template class PaddingLoDTensorFunctor { public: @@ -87,9 +103,8 @@ class PaddingLoDTensorFunctor { T* pad_data = pad_tensor->data(); const T* pad_value_data = pad_value.data(); if (pad_value.numel() == 1) { - for (int i = 0; i < pad_tensor->numel(); ++i) { - pad_data[i] = *pad_value_data; - } + fast_mem_init(pad_data, pad_tensor->numel(), pad_value_data, + sizeof(T)); } else { for (int i = 0; i < pad_tensor->numel(); i += step_width) { memcpy(pad_data + i, pad_value_data, step_width * sizeof(T)); diff --git a/paddle/fluid/operators/math/sequence_pooling.cc b/paddle/fluid/operators/math/sequence_pooling.cc index 7af44f2b2ca56f615ca0c8ad4590958af2abe9eb..011d45c396579a26a804a4cf2ecd50734e7df945 100644 --- a/paddle/fluid/operators/math/sequence_pooling.cc +++ b/paddle/fluid/operators/math/sequence_pooling.cc @@ -36,8 +36,8 @@ template class MaxSeqPoolFunctor { public: void operator()(const platform::CPUDeviceContext& context, - const framework::LoDTensor& input, framework::Tensor* output, - framework::Tensor* index) { + const framework::LoDTensor& input, T pad_value, + framework::Tensor* output, framework::Tensor* index) { auto in_dims = input.dims(); auto out_dims = output->dims(); auto idx_dims = index->dims(); @@ -56,6 +56,13 @@ class MaxSeqPoolFunctor { int64_t num_seq = out_dims[0]; int64_t dim = output->numel() / num_seq; for (int64_t i = 0; i < num_seq; ++i) { + if (starts[i] == starts[i + 1]) { + for (int64_t k = 0; k < dim; ++k) { + out_data[i * dim + k] = pad_value; + max_index[i * dim + k] = -1; + } + continue; + } for (int64_t k = 0; k < dim; ++k) { out_data[i * dim + k] = in_data[starts[i] * dim + k]; max_index[i * dim + k] = starts[i]; @@ -77,8 +84,8 @@ template class MaxSeqPoolFunctor { public: void operator()(const platform::CPUDeviceContext& context, - const framework::LoDTensor& input, framework::Tensor* output, - framework::Tensor* index) { + const framework::LoDTensor& input, T pad_value, + framework::Tensor* output, framework::Tensor* index) { auto in_dims = input.dims(); auto out_dims = output->dims(); PADDLE_ENFORCE_GT(in_dims.size(), 1); @@ -94,6 +101,12 @@ class MaxSeqPoolFunctor { int64_t num_seq = out_dims[0]; int64_t dim = output->numel() / num_seq; for (int64_t i = 0; i < num_seq; ++i) { + if (starts[i] == starts[i + 1]) { + for (int64_t k = 0; k < dim; ++k) { + out_data[i * dim + k] = pad_value; + } + continue; + } std::memcpy(&out_data[i * dim], &in_data[starts[i] * dim], dim * sizeof(T)); for (size_t j = starts[i] + 1; j < starts[i + 1]; ++j) { @@ -134,6 +147,7 @@ class MaxSeqPoolGradFunctor { for (int64_t i = 0; i < num_seq; ++i) { for (int64_t j = 0; j < dim; ++j) { int step_id = max_index[i * dim + j]; + if (step_id == -1) continue; ig_data[step_id * dim + j] = og_data[i * dim + j]; } } @@ -144,7 +158,7 @@ template class LastSeqPoolFunctor { public: void operator()(const platform::CPUDeviceContext& context, - const framework::LoDTensor& input, + const framework::LoDTensor& input, T pad_value, framework::Tensor* output) { // Create pointers to input and output data auto* in_data = input.data(); @@ -157,10 +171,16 @@ class LastSeqPoolFunctor { for (int i = 0; i < seq_num; ++i) { // Calculate the length of each sequence int64_t seq_len = static_cast(lod[i + 1] - lod[i]); - // Point to the begin of next sequence - in_data += seq_len * item_size; - // Copy the last item of sequence to output - std::memcpy(out_data, (in_data - item_size), item_size * sizeof(T)); + if (seq_len == 0) { + for (int j = 0; j < item_size; ++j) { + out_data[j] = pad_value; + } + } else { + // Point to the begin of next sequence + in_data += seq_len * item_size; + // Copy the last item of sequence to output + std::memcpy(out_data, (in_data - item_size), item_size * sizeof(T)); + } out_data += item_size; } } @@ -170,7 +190,7 @@ template class FirstSeqPoolFunctor { public: void operator()(const platform::CPUDeviceContext& context, - const framework::LoDTensor& input, + const framework::LoDTensor& input, T pad_value, framework::Tensor* output) { // Create pointers to input and output data auto* in_data = input.data(); @@ -183,10 +203,16 @@ class FirstSeqPoolFunctor { for (int i = 0; i < seq_num; ++i) { // Calculate the length of each sequence int64_t seq_len = static_cast(lod[i + 1] - lod[i]); - // Copy the first item of sequence to output - std::memcpy(out_data, in_data, item_size * sizeof(T)); - // Point to the next sequence - in_data += seq_len * item_size; + if (seq_len == 0) { + for (int j = 0; j < item_size; ++j) { + out_data[j] = pad_value; + } + } else { + // Copy the first item of sequence to output + std::memcpy(out_data, in_data, item_size * sizeof(T)); + // Point to the next sequence + in_data += seq_len * item_size; + } out_data += item_size; } } @@ -207,6 +233,7 @@ class SumSeqPoolGradFunctor { auto blas = math::GetBlas(context); for (int i = 0; i < static_cast(lod.size()) - 1; ++i) { int64_t h = static_cast(lod[i + 1] - lod[i]); + if (h == 0) continue; int64_t in_offset = lod[i] * in_w; const T* out_pos = out_g_data + i * out_w; T* in_pos = in_g_data + in_offset; @@ -222,27 +249,27 @@ class SequencePoolFunctor { public: /* max pool has index output */ void operator()(const platform::CPUDeviceContext& context, - const std::string pooltype, const framework::LoDTensor& input, - framework::Tensor* output, bool is_test, - framework::Tensor* index = nullptr) { + const std::string pooltype, T pad_value, + const framework::LoDTensor& input, framework::Tensor* output, + bool is_test, framework::Tensor* index = nullptr) { if (pooltype == "MAX") { if (is_test) { math::MaxSeqPoolFunctor max_pool; - max_pool(context, input, output, index); + max_pool(context, input, pad_value, output, index); } else { math::MaxSeqPoolFunctor max_pool; - max_pool(context, input, output, index); + max_pool(context, input, pad_value, output, index); } return; } if (pooltype == "LAST") { math::LastSeqPoolFunctor last_pool; - last_pool(context, input, output); + last_pool(context, input, pad_value, output); return; } if (pooltype == "FIRST") { math::FirstSeqPoolFunctor first_pool; - first_pool(context, input, output); + first_pool(context, input, pad_value, output); return; } @@ -260,7 +287,13 @@ class SequencePoolFunctor { .At(attr); for (int i = 0; i < static_cast(lod.size()) - 1; ++i) { attr.h = static_cast(lod[i + 1] - lod[i]); - seqpool(src, dst, &attr); + if (attr.h == 0) { + for (int j = 0; j < attr.w; ++j) { + dst[j] = pad_value; + } + } else { + seqpool(src, dst, &attr); + } dst += attr.w; src += attr.h * attr.w; } @@ -268,11 +301,17 @@ class SequencePoolFunctor { } auto& place = *context.eigen_device(); for (int i = 0; i < static_cast(lod.size()) - 1; ++i) { + Tensor out_t = output->Slice(i, i + 1); + int64_t w = input.numel() / input.dims()[0]; + if (lod[i] == lod[i + 1]) { + for (int j = 0; j < w; ++j) { + out_t.data()[j] = pad_value; + } + continue; + } Tensor in_t = input.Slice(static_cast(lod[i]), static_cast(lod[i + 1])); - Tensor out_t = output->Slice(i, i + 1); int64_t h = static_cast(lod[i + 1] - lod[i]); - int64_t w = input.numel() / input.dims()[0]; auto in_e = EigenMatrix::From(in_t, framework::make_ddim({h, w})); auto out_e = EigenVector::Flatten(out_t); if (pooltype == "AVERAGE") { @@ -316,6 +355,7 @@ class SequencePoolGradFunctor { auto lod = in_grad->lod()[0]; auto& place = *context.eigen_device(); for (int i = 0; i < static_cast(lod.size()) - 1; ++i) { + if (lod[i] == lod[i + 1]) continue; auto in_g_t = in_grad->Slice(static_cast(lod[i]), static_cast(lod[i + 1])); auto out_g_t = out_grad.Slice(i, i + 1); diff --git a/paddle/fluid/operators/math/sequence_pooling.cu b/paddle/fluid/operators/math/sequence_pooling.cu index 51da6de26e2a47da2c22a1c2e2e1a9412badc58f..4de99ba677d5108e8b70e71e3dfefa17b6e18beb 100644 --- a/paddle/fluid/operators/math/sequence_pooling.cu +++ b/paddle/fluid/operators/math/sequence_pooling.cu @@ -24,96 +24,122 @@ namespace math { template struct MaxPoolFunctor { - HOSTDEVICE void operator()(const T* input, const size_t start, - const size_t end, const size_t item_dim, T* output, - int* index) { + HOSTDEVICE void operator()(const T* input, const T pad_value, + const size_t start, const size_t end, + const size_t item_dim, T* output, int* index) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { T max_val = static_cast(-FLT_MAX); int max_index = -1; - for (int i = start; i < end; ++i) { - if (max_val < input[item_dim * i + tid]) { - max_val = input[item_dim * i + tid]; - max_index = i; + if (start == end) { + output[tid] = pad_value; + index[tid] = -1; + } else { + for (int i = start; i < end; ++i) { + if (max_val < input[item_dim * i + tid]) { + max_val = input[item_dim * i + tid]; + max_index = i; + } } + output[tid] = max_val; + index[tid] = max_index; } - output[tid] = max_val; - index[tid] = max_index; } } }; template struct AvgPoolFunctor { - HOSTDEVICE void operator()(const T* input, const size_t start, - const size_t end, const size_t item_dim, T* output, - int* index) { + HOSTDEVICE void operator()(const T* input, const T pad_value, + const size_t start, const size_t end, + const size_t item_dim, T* output, int* index) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { - T val = static_cast(0); - for (int i = start; i < end; ++i) { - val += input[item_dim * i + tid]; + if (start == end) { + output[tid] = pad_value; + } else { + T val = static_cast(0); + for (int i = start; i < end; ++i) { + val += input[item_dim * i + tid]; + } + // end, start is lod, so end - start != 0 + output[tid] = val / static_cast(end - start); } - // end, start is lod, so end - start != 0 - output[tid] = val / static_cast(end - start); } } }; template struct SumPoolFunctor { - HOSTDEVICE void operator()(const T* input, const size_t start, - const size_t end, const size_t item_dim, T* output, - int* index) { + HOSTDEVICE void operator()(const T* input, const T pad_value, + const size_t start, const size_t end, + const size_t item_dim, T* output, int* index) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { - T val = static_cast(0); - for (int i = start; i < end; ++i) { - val += input[item_dim * i + tid]; + if (start == end) { + output[tid] = pad_value; + } else { + T val = static_cast(0); + for (int i = start; i < end; ++i) { + val += input[item_dim * i + tid]; + } + output[tid] = val; } - output[tid] = val; } } }; template struct SqrtPoolFunctor { - HOSTDEVICE void operator()(const T* input, const size_t start, - const size_t end, const size_t item_dim, T* output, - int* index) { + HOSTDEVICE void operator()(const T* input, const T pad_value, + const size_t start, const size_t end, + const size_t item_dim, T* output, int* index) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { - T val = static_cast(0); - for (int i = start; i < end; ++i) { - val += input[item_dim * i + tid]; + if (start == end) { + output[tid] = pad_value; + } else { + T val = static_cast(0); + for (int i = start; i < end; ++i) { + val += input[item_dim * i + tid]; + } + // end, start is lod, so end - start != 0 + output[tid] = val / sqrt(end - start); } - // end, start is lod, so end - start != 0 - output[tid] = val / sqrt(end - start); } } }; template struct LastPoolFunctor { - HOSTDEVICE void operator()(const T* input, const size_t start, - const size_t end, const size_t item_dim, T* output, - int* index) { + HOSTDEVICE void operator()(const T* input, const T pad_value, + const size_t start, const size_t end, + const size_t item_dim, T* output, int* index) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { - output[tid] = input[item_dim * (end - 1) + tid]; + if (start == end) { + output[tid] = pad_value; + } else { + output[tid] = input[item_dim * (end - 1) + tid]; + } } } }; template struct FirstPoolFunctor { - HOSTDEVICE void operator()(const T* input, const size_t start, - const size_t end, const size_t item_dim, T* output, - int* index) { + HOSTDEVICE void operator()(const T* input, const T pad_value, + const size_t start, const size_t end, + const size_t item_dim, T* output, int* index) { for (int tid = threadIdx.x; tid < item_dim; tid += blockDim.x) { - output[tid] = input[item_dim * start + tid]; + if (start == end) { + output[tid] = pad_value; + } else { + output[tid] = input[item_dim * start + tid]; + } } } }; template __global__ void sequence_pool_kernel(Range_OP op, const T* input, - const size_t* lod, const size_t lod_size, + const T pad_value, const size_t* lod, + const size_t lod_size, const size_t item_dim, T* output, int* index) { int bid = blockIdx.x; @@ -124,16 +150,17 @@ __global__ void sequence_pool_kernel(Range_OP op, const T* input, if (index != nullptr) { index_offset = &index[bid * item_dim]; } - op(input, start, end, item_dim, &output[bid * item_dim], index_offset); + op(input, pad_value, start, end, item_dim, &output[bid * item_dim], + index_offset); } template class SequencePoolFunctor { public: void operator()(const platform::CUDADeviceContext& context, - const std::string pooltype, const framework::LoDTensor& input, - framework::Tensor* output, bool is_test, - framework::Tensor* index = nullptr) { + const std::string pooltype, T pad_value, + const framework::LoDTensor& input, framework::Tensor* output, + bool is_test, framework::Tensor* index = nullptr) { auto& lod = input.lod()[0]; const size_t item_dim = output->numel() / output->dims()[0]; dim3 threads(1024, 1); @@ -141,37 +168,37 @@ class SequencePoolFunctor { if (pooltype == "MAX") { sequence_pool_kernel< T, MaxPoolFunctor><<>>( - MaxPoolFunctor(), input.data(), + MaxPoolFunctor(), input.data(), pad_value, lod.CUDAData(context.GetPlace()), lod.size(), item_dim, output->mutable_data(context.GetPlace()), index->data()); } else if (pooltype == "AVERAGE") { sequence_pool_kernel< T, AvgPoolFunctor><<>>( - AvgPoolFunctor(), input.data(), + AvgPoolFunctor(), input.data(), pad_value, lod.CUDAData(context.GetPlace()), lod.size(), item_dim, output->mutable_data(context.GetPlace()), nullptr); } else if (pooltype == "SUM") { sequence_pool_kernel< T, SumPoolFunctor><<>>( - SumPoolFunctor(), input.data(), + SumPoolFunctor(), input.data(), pad_value, lod.CUDAData(context.GetPlace()), lod.size(), item_dim, output->mutable_data(context.GetPlace()), nullptr); } else if (pooltype == "SQRT") { sequence_pool_kernel< T, SqrtPoolFunctor><<>>( - SqrtPoolFunctor(), input.data(), + SqrtPoolFunctor(), input.data(), pad_value, lod.CUDAData(context.GetPlace()), lod.size(), item_dim, output->mutable_data(context.GetPlace()), nullptr); } else if (pooltype == "LAST") { sequence_pool_kernel< T, LastPoolFunctor><<>>( - LastPoolFunctor(), input.data(), + LastPoolFunctor(), input.data(), pad_value, lod.CUDAData(context.GetPlace()), lod.size(), item_dim, output->mutable_data(context.GetPlace()), nullptr); } else if (pooltype == "FIRST") { sequence_pool_kernel< T, FirstPoolFunctor><<>>( - FirstPoolFunctor(), input.data(), + FirstPoolFunctor(), input.data(), pad_value, lod.CUDAData(context.GetPlace()), lod.size(), item_dim, output->mutable_data(context.GetPlace()), nullptr); } else { diff --git a/paddle/fluid/operators/math/sequence_pooling.h b/paddle/fluid/operators/math/sequence_pooling.h index a1046ea2160d0ae9c2251612c97d3f2640b0aad1..1dc02eae201413b9483b31129578be144f175aa3 100644 --- a/paddle/fluid/operators/math/sequence_pooling.h +++ b/paddle/fluid/operators/math/sequence_pooling.h @@ -27,8 +27,9 @@ class SequencePoolFunctor { public: /* max pool has index output */ void operator()(const DeviceContext& context, const std::string pooltype, - const framework::LoDTensor& input, framework::Tensor* output, - bool is_test = false, framework::Tensor* index = nullptr); + T pad_value, const framework::LoDTensor& input, + framework::Tensor* output, bool is_test = false, + framework::Tensor* index = nullptr); }; template diff --git a/paddle/fluid/operators/math/softmax.h b/paddle/fluid/operators/math/softmax.h index a7a30a71e4cf176987cc75be1958a762a08b09ae..7a4306efef97ea572f90929cd79f4b9092a64d1f 100644 --- a/paddle/fluid/operators/math/softmax.h +++ b/paddle/fluid/operators/math/softmax.h @@ -27,7 +27,7 @@ class SoftmaxFunctor { const framework::Tensor* X, framework::Tensor* Y); }; -template +template class SoftmaxGradFunctor { public: void operator()(const DeviceContext& context, const int axis_dim, diff --git a/paddle/fluid/operators/math/softmax_impl.h b/paddle/fluid/operators/math/softmax_impl.h index 6f6f33345f5336a8b8ff100c0286914ef629283f..4fb03cdce0c78ed72e69e3d70e836ee8a914110a 100644 --- a/paddle/fluid/operators/math/softmax_impl.h +++ b/paddle/fluid/operators/math/softmax_impl.h @@ -17,6 +17,8 @@ limitations under the License. */ #include "paddle/fluid/framework/eigen.h" #include "paddle/fluid/framework/tensor.h" #include "paddle/fluid/operators/jit/kernels.h" +#include "paddle/fluid/operators/math/cpu_vec.h" +#include "paddle/fluid/platform/cpu_info.h" namespace paddle { namespace operators { @@ -34,16 +36,15 @@ struct ValueClip { } }; -template -void SoftmaxFunctor::operator()( - const DeviceContext& context, const int axis_dim, - const framework::Tensor* X, framework::Tensor* Y) { +template +void SoftmaxEigen(const DeviceContext& context, const int axis_dim, + const framework::Tensor* X, framework::Tensor* Y) { + constexpr int kBatchDim = 0; + constexpr int kClassDim = 1; + auto logits = EigenMatrix::From(*X); auto softmax = EigenMatrix::From(*Y); - const int kBatchDim = 0; - const int kClassDim = 1; - const int batch_size = logits.dimension(kBatchDim); const int num_classes = logits.dimension(kClassDim); const int num_remain = num_classes / axis_dim; @@ -70,12 +71,58 @@ void SoftmaxFunctor::operator()( .broadcast(one_axis)); } +template +void SoftmaxFunctor::operator()( + const DeviceContext& context, const int axis_dim, + const framework::Tensor* X, framework::Tensor* Y) { + SoftmaxEigen(context, axis_dim, X, Y); +} + template using enable_if_CPU = typename std::enable_if< std::is_same::value>::type; +template +class SoftmaxFunctor> { + public: + void operator()(const DeviceContext& context, const int axis_dim, + const framework::Tensor* X, framework::Tensor* Y) { + auto in_dims = X->dims(); + constexpr int kBatchDim = 0; + constexpr int kClassDim = 1; + + const int num_classes = in_dims[kClassDim]; + const int batch_size = in_dims[kBatchDim]; + const int num_remain = num_classes / axis_dim; + + if (num_remain == 1 && platform::MayIUse(platform::avx)) { + const T* in_data = X->data(); + T* out_data = Y->data(); + for (int bs = 0; bs < batch_size; ++bs) { + T max_val = *std::max_element(in_data, in_data + num_classes); + max_val *= static_cast(-1); + vec_add_bias(num_classes, max_val, in_data, out_data); + vec_clip(num_classes, static_cast(-64), out_data, + out_data); + vec_exp(num_classes, out_data, out_data); + + T sum = 0; + vec_sum(num_classes, out_data, &sum); + sum = static_cast(1) / sum; + vec_scal(num_classes, sum, out_data, out_data); + + in_data += num_classes; + out_data += num_classes; + } + } else { + SoftmaxEigen(context, axis_dim, X, Y); + } + } +}; + template class SoftmaxFunctor> { + public: void operator()(const DeviceContext& context, const int axis_dim, const framework::Tensor* X, framework::Tensor* Y) { auto in_dims = X->dims(); @@ -93,16 +140,16 @@ class SoftmaxFunctor> { }; template -void SoftmaxGradFunctor::operator()( - const DeviceContext& context, const int axis_dim, - const framework::Tensor* y, const framework::Tensor* y_grad, - framework::Tensor* x_grad) { +void SoftmaxGradEigen(const DeviceContext& context, const int axis_dim, + const framework::Tensor* y, + const framework::Tensor* y_grad, + framework::Tensor* x_grad) { auto softmax = EigenMatrix::From(*y); auto softmax_grad = EigenMatrix::From(*y_grad); auto logits_grad = EigenMatrix::From(*x_grad); - const int kBatchDim = 0; - const int kClassDim = 1; + constexpr int kBatchDim = 0; + constexpr int kClassDim = 1; const int batch_size = softmax.dimension(kBatchDim); const int num_classes = softmax.dimension(kClassDim); @@ -122,6 +169,48 @@ void SoftmaxGradFunctor::operator()( logits_grad.device(*context.eigen_device()) = (softmax_grad - dot) * softmax; } +template +void SoftmaxGradFunctor::operator()( + const DeviceContext& context, const int axis_dim, + const framework::Tensor* y, const framework::Tensor* y_grad, + framework::Tensor* x_grad) { + SoftmaxGradEigen(context, axis_dim, y, y_grad, x_grad); +} + +template +class SoftmaxGradFunctor> { + public: + void operator()(const DeviceContext& context, const int axis_dim, + const framework::Tensor* y, const framework::Tensor* y_grad, + framework::Tensor* x_grad) { + auto out_dims = y->dims(); + constexpr int kBatchDim = 0; + constexpr int kClassDim = 1; + const int num_classes = out_dims[kClassDim]; + const int batch_size = out_dims[kBatchDim]; + const int num_remain = num_classes / axis_dim; + + if (num_remain == 1 && platform::MayIUse(platform::avx)) { + const T* out_data = y->data(); + const T* out_grad = y_grad->data(); + T* in_grad = x_grad->data(); + for (int bs = 0; bs < batch_size; ++bs) { + T scalar; + vec_mul_reduce(num_classes, out_grad, out_data, + &scalar); + scalar *= static_cast(-1); + vec_add_bias(num_classes, scalar, out_grad, in_grad); + vec_mul(num_classes, out_data, in_grad, in_grad); + out_data += num_classes; + out_grad += num_classes; + in_grad += num_classes; + } + } else { + SoftmaxGradEigen(context, axis_dim, y, y_grad, x_grad); + } + } +}; + } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc index 911c4d22ee5cd84c0b42646a1d3e62a0d765732e..40f7231c125d3da7764d63cd1ab7c631219c722d 100644 --- a/paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc @@ -61,20 +61,25 @@ class BatchNormMKLDNNHandler : public platform::MKLDNNHandler { std::shared_ptr AcquireBatchNormPrimitiveDescriptor(const batch_norm_fwd::desc &bn_fwd_desc, const mkldnn::engine &engine) { - const std::string key_batch_norm_fwd_pd = key_ + "@bn_fwd_pd"; - auto batch_norm_pd = - std::static_pointer_cast( - dev_ctx_.GetBlob(key_batch_norm_fwd_pd)); - - if (batch_norm_pd == nullptr) { - batch_norm_pd_.reset( - new batch_norm_fwd::primitive_desc(bn_fwd_desc, engine)); - dev_ctx_.SetBlob(key_batch_norm_fwd_pd, batch_norm_pd_); - } else { - batch_norm_pd_ = batch_norm_pd; - is_reusing_ = true; + // BatchNorm PD has to be passed to Grad op that + // may be executed by diffrent thread, hence + // for that one we use key that does not contain TID + const std::string key_batch_norm_fwd_pd = key_common_ + "@bn_fwd_pd"; + batch_norm_pd_ = std::static_pointer_cast( + dev_ctx_.GetBlob(key_batch_norm_fwd_pd)); + + if (batch_norm_pd_ == nullptr) { + static std::mutex acquire_barrier; + std::lock_guard block_threads_until_finish_this_job( + acquire_barrier); + batch_norm_pd_ = std::static_pointer_cast( + dev_ctx_.GetBlob(key_batch_norm_fwd_pd)); + if (batch_norm_pd_ == nullptr) { + batch_norm_pd_.reset( + new batch_norm_fwd::primitive_desc(bn_fwd_desc, engine)); + dev_ctx_.SetBlob(key_batch_norm_fwd_pd, batch_norm_pd_); + } } - return batch_norm_pd_; } @@ -87,9 +92,6 @@ class BatchNormMKLDNNHandler : public platform::MKLDNNHandler { auto batch_norm_p = std::static_pointer_cast(dev_ctx_.GetBlob(prim_key)); - PADDLE_ENFORCE((batch_norm_p != nullptr) || !is_reusing_, - "Fail to find batch norm primitive in device context"); - if (batch_norm_p == nullptr) { if (is_test) { batch_norm_p = std::make_shared( @@ -104,8 +106,6 @@ class BatchNormMKLDNNHandler : public platform::MKLDNNHandler { } dev_ctx_.SetBlob(prim_key, batch_norm_p); - } else { - is_reusing_ = true; } return batch_norm_p; diff --git a/paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc index 50fe2e6e4c5a5e3e0ed1d9a9827e75094454c2fc..a855ba8475a1b0ce6d5af8f4d3753723c2968942 100644 --- a/paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc @@ -79,6 +79,8 @@ std::string CreateKey(const paddle::framework::ExecutionContext& ctx, platform::MKLDNNHandler::AppendKey(&key, std::to_string(concat_axis)); platform::MKLDNNHandler::AppendKey(&key, ctx.op().Output("Out")); platform::MKLDNNHandler::AppendKey(&key, std::to_string(dt)); + platform::MKLDNNHandler::AppendKey(&key, + std::to_string(multi_input[0]->format())); return key; } diff --git a/paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc index faf518005c8cb0958dd5b0bbfc5c6fc4b3c2b582..647e09a92911e327ba01b7bb23fdb617f949cea4 100644 --- a/paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc @@ -119,9 +119,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { std::vector dilations = ctx.Attr>("dilations"); bool fuse_relu = ctx.Attr("fuse_relu"); bool fuse_residual_conn = ctx.Attr("fuse_residual_connection"); + bool fuse_brelu = false; + float fuse_brelu_threshold = 6.0; int groups = ctx.Attr("groups"); - bool is_conv3d = strides.size() == 3U; + if (!is_conv3d) { + fuse_brelu = ctx.Attr("fuse_brelu"); + fuse_brelu_threshold = ctx.Attr("fuse_brelu_threshold"); + } // TODO(tpatejko): add support for dilation PADDLE_ENFORCE( is_conv3d @@ -142,8 +147,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { // Get unique name for storing MKLDNN primitives const std::string key = platform::ConvMKLDNNHandler::GetHash( - src_tz, weights_tz, strides, paddings, dilations, groups, - ctx.op().Input("Input") + ctx.op().Input("Filter")); + src_tz, weights_tz, fuse_relu, fuse_brelu, strides, paddings, dilations, + groups, ctx.op().Input("Input") + ctx.op().Input("Filter")); std::vector pipeline; @@ -194,11 +199,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { bias_tz, platform::MKLDNNGetDataType(), memory::format::x); conv_pd = handler.AcquireConvolutionPrimitiveDescriptor( src_md, weights_md, bias_md, dst_md, strides, paddings, mkldnn_engine, - fuse_relu, fuse_residual_conn, fwd_prop_kind); + fuse_relu, fuse_residual_conn, fuse_brelu, fuse_brelu_threshold, + fwd_prop_kind); } else { conv_pd = handler.AcquireConvolutionPrimitiveDescriptor( src_md, weights_md, boost::none, dst_md, strides, paddings, - mkldnn_engine, fuse_relu, fuse_residual_conn, fwd_prop_kind); + mkldnn_engine, fuse_relu, fuse_residual_conn, fuse_brelu, + fuse_brelu_threshold, fwd_prop_kind); } // create mkldnn memory from input tensors (data/weights) @@ -227,9 +234,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { "same dimension sizes"); if (residual_param->format() != handler.GetDstFormat()) { - auto output_data = output->mutable_data( - ctx.GetPlace(), ::paddle::memory::Allocator::kDefault, - handler.GetDstMemorySize()); + auto output_data = + output->mutable_data(ctx.GetPlace(), handler.GetDstMemorySize()); auto residual_data_tz = paddle::framework::vectorize2int(residual_param->dims()); auto residual_data_type = @@ -249,9 +255,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { handler.AcquireDstMemoryFromPrimitive(to_void_cast(output_data)); } } else { - auto output_data = output->mutable_data( - ctx.GetPlace(), paddle::memory::Allocator::kDefault, - handler.GetDstMemorySize()); + auto output_data = + output->mutable_data(ctx.GetPlace(), handler.GetDstMemorySize()); dst_memory_p = handler.AcquireDstMemoryFromPrimitive(to_void_cast(output_data)); } @@ -281,6 +286,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { output->set_layout(DataLayout::kMKLDNN); output->set_format(GetMKLDNNFormat(*dst_memory_p)); } + void ComputeINT8(const paddle::framework::ExecutionContext& ctx) const { const bool is_test = ctx.Attr("is_test"); @@ -317,13 +323,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { int groups = ctx.Attr("groups"); bool fuse_relu = ctx.Attr("fuse_relu"); bool fuse_residual_conn = ctx.Attr("fuse_residual_connection"); - + bool fuse_brelu = ctx.Attr("fuse_brelu"); + float fuse_brelu_threshold = ctx.Attr("fuse_brelu_threshold"); bool force_fp32_output = ctx.Attr("force_fp32_output"); + bool unsigned_output = fuse_relu || fuse_brelu; if (fuse_residual_conn) { PADDLE_ENFORCE(force_fp32_output != true, "residual fusion does not support force output with fp32"); } - bool is_conv3d = strides.size() == 3U; // TODO(tpatejko): add support for dilation PADDLE_ENFORCE( @@ -341,15 +348,18 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { std::vector weights_tz = paddle::framework::vectorize2int(filter->dims()); int g = std::max(groups, 1); + GetWeightsTz(weights_tz, g, is_conv3d); std::vector dst_tz = paddle::framework::vectorize2int(output->dims()); mkldnn::memory::data_type src_dt = paddle::framework::ToMKLDNNDataType(input->type()); - auto dst_dt = fuse_relu ? paddle::framework::ToMKLDNNDataType( - framework::DataTypeTrait::DataType) - : paddle::framework::ToMKLDNNDataType( - framework::DataTypeTrait::DataType); + + auto dst_dt = unsigned_output + ? paddle::framework::ToMKLDNNDataType( + framework::DataTypeTrait::DataType) + : paddle::framework::ToMKLDNNDataType( + framework::DataTypeTrait::DataType); if (force_fp32_output) { dst_dt = paddle::framework::ToMKLDNNDataType( @@ -367,20 +377,19 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { key.reserve(MaxKeyLength); platform::ConvMKLDNNHandler::AppendKey( &key, src_tz, weights_tz, strides, paddings, dilations, groups, src_dt, - input->format(), fuse_relu, fuse_residual_conn, + input->format(), fuse_relu, fuse_residual_conn, fuse_brelu, ctx.op().Input("Input") + ctx.op().Input("Filter")); + const std::string key_conv_pd = key + "@conv_pd"; bool need_s8_to_u8 = false; - - std::shared_ptr conv_p = nullptr; - std::shared_ptr src_memory_p = nullptr; - std::shared_ptr user_src_memory_p = nullptr; - std::shared_ptr dst_memory_p = nullptr; + std::shared_ptr conv_p; + std::shared_ptr src_memory_p; + std::shared_ptr user_src_memory_p; + std::shared_ptr dst_memory_p; std::vector pipeline; - std::shared_ptr conv_pd = - nullptr; - std::shared_ptr handler = nullptr; + std::shared_ptr conv_pd; + std::shared_ptr handler; auto prim_key = key + "@conv_p"; auto dst_key = key + "@dst_mem_p"; @@ -417,7 +426,9 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { // scale couldn't be calculated else output_shift_scale[i] = - scale_out_data / (scale_in_data * scale_weights_data[i]); + static_cast(static_cast(scale_out_data) / + (static_cast(scale_in_data) * + static_cast(scale_weights_data[i]))); } auto user_src_md = @@ -445,26 +456,24 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { platform::MKLDNNMemDesc(dst_tz, dst_dt, chosen_memory_format); // create a conv primitive descriptor and save it for usage in backward + // TODO(lidanqing): We use relu post-op instead of brelu post-op cause + // mkldnn v0.18 does not support INT8 brelu post-op. Use code in /**/ when + // v0.20 is enabled + std::shared_ptr bias_md_p; if (bias) { bias_tz = paddle::framework::vectorize2int(bias->dims()); - auto bias_md = platform::MKLDNNMemDesc(bias_tz, memory::data_type::s32, - memory::format::x); - conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, bias_md, dst_md, - strides, paddings, mkldnn_engine, - fuse_relu, fuse_residual_conn, - output_shift_scale, sum_scale, is_test); - } else { - conv_pd = - ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides, paddings, - mkldnn_engine, fuse_relu, fuse_residual_conn, - output_shift_scale, sum_scale, is_test); + bias_md_p = std::make_shared(platform::MKLDNNMemDesc( + bias_tz, memory::data_type::s32, memory::format::x)); } + conv_pd = ConvFwdPrimitiveDesc( + src_md, weights_md, bias_md_p, dst_md, strides, paddings, + mkldnn_engine, fuse_relu || fuse_brelu /*fuse_relu*/, + fuse_residual_conn, false /*fuse_brelu*/, fuse_brelu_threshold, + output_shift_scale, sum_scale, is_test); // Save conv_pd/src_memory/weights_memory for backward pass dev_ctx.SetBlob(key_conv_pd, conv_pd); - handler.reset(new platform::ConvMKLDNNHandler(conv_pd, dev_ctx, mkldnn_engine, key)); - // create mkldnn memory from input tensors (data/weights) user_src_memory_p = handler->AcquireSrcMemory(user_src_md, to_void_cast(input_data)); @@ -501,7 +510,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { ctx, output, residual_param, user_residual_md, handler, &pipeline); } else { - need_s8_to_u8 = fuse_relu; + need_s8_to_u8 = unsigned_output; dst_memory_p = platform::SetDstMemory( ctx, output, residual_param, user_residual_md, handler, &pipeline); @@ -512,12 +521,12 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { dst_memory_p = platform::SetDstMemory(ctx, output, handler); } else { - need_s8_to_u8 = fuse_relu; + need_s8_to_u8 = unsigned_output; dst_memory_p = platform::SetDstMemory(ctx, output, handler); } } } else if (!force_fp32_output) { - if (fuse_relu) { + if (unsigned_output) { dst_memory_p = platform::SetDstMemory(ctx, output, handler); } else { dst_memory_p = platform::SetDstMemory(ctx, output, handler); @@ -589,12 +598,12 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { platform::SetDstMemoryHandler(ctx, output, handler, &dst_memory_p); } else { - need_s8_to_u8 = fuse_relu; + need_s8_to_u8 = unsigned_output; platform::SetDstMemoryHandler(ctx, output, handler, &dst_memory_p); } } else if (!force_fp32_output) { - if (fuse_relu) { + if (unsigned_output) { platform::SetDstMemoryHandler(ctx, output, handler, &dst_memory_p); } else { @@ -632,11 +641,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { private: mkldnn::primitive_attr CreatePostOps( bool fuse_relu, bool fuse_residual_conn, - const std::vector output_shift_scale, float sum_scale) const { + const std::vector& output_shift_scale, float sum_scale, + bool fuse_brelu, float fuse_brelu_threshold) const { mkldnn::primitive_attr conv_attr; mkldnn::post_ops post_operations; int mask = output_shift_scale.size() > 1 ? 1 << 1 : 0; conv_attr.set_output_scales(mask, output_shift_scale); + if (fuse_residual_conn) { post_operations.append_sum(sum_scale); } @@ -647,59 +658,46 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel { post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu, negative_slope, placeholder); } + if (fuse_brelu) { + constexpr float scale = 1.0f; + constexpr float placeholder = 0.0f; // beta + post_operations.append_eltwise(scale, + mkldnn::algorithm::eltwise_bounded_relu, + fuse_brelu_threshold, placeholder); + } conv_attr.set_post_ops(post_operations); return conv_attr; } std::unique_ptr ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights, + const std::shared_ptr bias_md_p, const memory::desc& dst, const std::vector& strides, const std::vector& paddings, const mkldnn::engine& engine, const bool fuse_relu, - const bool fuse_residual_conn, - const std::vector output_shift_scale, + const bool fuse_residual_conn, const bool fuse_brelu, + const float fuse_brelu_threshold, + const std::vector& output_shift_scale, const float sum_scale, bool is_test) const { memory::dims stride_dims = {strides[0], strides[1]}; memory::dims padding_dims = {paddings[0], paddings[1]}; auto propagation = is_test ? mkldnn::prop_kind::forward_scoring : mkldnn::prop_kind::forward_training; - - auto conv_desc = mkldnn::convolution_forward::desc( - propagation, mkldnn::convolution_direct, src, weights, dst, stride_dims, - padding_dims, padding_dims, mkldnn::padding_kind::zero); - - mkldnn::primitive_attr conv_attr = CreatePostOps( - fuse_relu, fuse_residual_conn, output_shift_scale, sum_scale); - - auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc( - conv_desc, conv_attr, engine); - - return std::unique_ptr( - p_conv_pd); - } - - std::unique_ptr - ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights, - const memory::desc& bias, const memory::desc& dst, - const std::vector& strides, - const std::vector& paddings, - const mkldnn::engine& engine, const bool fuse_relu, - const bool fuse_residual_conn, - const std::vector output_shift_scale, - const float sum_scale, bool is_test) const { - memory::dims stride_dims = {strides[0], strides[1]}; - memory::dims padding_dims = {paddings[0], paddings[1]}; - - auto propagation = is_test ? mkldnn::prop_kind::forward_scoring - : mkldnn::prop_kind::forward_training; - - auto conv_desc = mkldnn::convolution_forward::desc( - propagation, mkldnn::convolution_direct, src, weights, bias, dst, - stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero); - - mkldnn::primitive_attr conv_attr = CreatePostOps( - fuse_relu, fuse_residual_conn, output_shift_scale, sum_scale); + auto conv_desc = + (bias_md_p != nullptr) + ? mkldnn::convolution_forward::desc( + propagation, mkldnn::convolution_direct, src, weights, + (*bias_md_p), dst, stride_dims, padding_dims, padding_dims, + mkldnn::padding_kind::zero) + : mkldnn::convolution_forward::desc( + propagation, mkldnn::convolution_direct, src, weights, dst, + stride_dims, padding_dims, padding_dims, + mkldnn::padding_kind::zero); + + mkldnn::primitive_attr conv_attr = + CreatePostOps(fuse_relu, fuse_residual_conn, output_shift_scale, + sum_scale, fuse_brelu, fuse_brelu_threshold); auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc( conv_desc, conv_attr, engine); @@ -762,7 +760,11 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel { GetWeightsTz(weights_tz, g, is_conv3d); std::vector dst_tz = paddle::framework::vectorize2int(output_grad->dims()); - + bool fuse_relu = ctx.Attr("fuse_relu"); + bool fuse_brelu = false; + if (!is_conv3d) { + fuse_brelu = ctx.Attr("fuse_brelu"); + } auto src_format = input->format(); mkldnn::memory::format weights_format = GetWeightsFormat(filter->format(), g, is_conv3d); @@ -771,8 +773,8 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel { // as well as attributes of primitive to be created // This name will be used as key when saving info into device context const std::string key = platform::ConvMKLDNNHandler::GetHash( - src_tz, weights_tz, strides, paddings, dilations, groups, - ctx.op().Input("Input") + ctx.op().Input("Filter")); + src_tz, weights_tz, fuse_relu, fuse_brelu, strides, paddings, dilations, + groups, ctx.op().Input("Input") + ctx.op().Input("Filter")); const std::string key_conv_pd = key + "@conv_pd"; std::vector pipeline; @@ -858,8 +860,7 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel { user_diff_dst_memory_p, pipeline); const size_t size = handler.GetDiffWeightsMemorySize(); - filter_grad_data = filter_grad->mutable_data( - ctx.GetPlace(), paddle::memory::Allocator::kDefault, size); + filter_grad_data = filter_grad->mutable_data(ctx.GetPlace(), size); auto diff_weights_memory_p = handler.AcquireDiffWeightsMemoryFromWeightsPrimitive( @@ -884,8 +885,7 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel { pipeline); const size_t size = handler.GetDiffSourceMemorySize(); - input_grad_data = input_grad->mutable_data( - ctx.GetPlace(), paddle::memory::Allocator::kDefault, size); + input_grad_data = input_grad->mutable_data(ctx.GetPlace(), size); auto diff_src_memory_p = handler.AcquireDiffSrcMemoryFromDataPrimitive( reinterpret_cast(input_grad_data)); diff --git a/paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc index 30d2469eeaf6938f1f93730b8b645ca2cfe97364..6d5982ab3f8ab65e3480dcf905dd8901759f90e0 100644 --- a/paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc @@ -166,11 +166,11 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel { bias_tz, platform::MKLDNNGetDataType(), mkldnn::memory::format::x); conv_transpose_pd = handler.AcquireConvolutionPrimitiveDescriptor( src_md, weights_md, bias_md, dst_md, strides, paddings, mkldnn_engine, - fuse_relu, false, fwd_prop_kind); + fuse_relu, false, false, 0.0, fwd_prop_kind); } else { conv_transpose_pd = handler.AcquireConvolutionPrimitiveDescriptor( src_md, weights_md, boost::none, dst_md, strides, paddings, - mkldnn_engine, fuse_relu, false, fwd_prop_kind); + mkldnn_engine, fuse_relu, false, false, 0.0, fwd_prop_kind); } // create mkldnn memory from input tensors (data/weights) @@ -188,9 +188,8 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel { std::shared_ptr dst_memory_p; - auto output_data = output->mutable_data( - ctx.GetPlace(), paddle::memory::Allocator::kDefault, - handler.GetDstMemorySize()); + auto output_data = + output->mutable_data(ctx.GetPlace(), handler.GetDstMemorySize()); dst_memory_p = handler.AcquireDstMemoryFromPrimitive( platform::to_void_cast(output_data)); diff --git a/paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc index 69c0486eb63475d759b6869f55d14ef1bec08b59..b525eaac3ef87f663a4a22c32017a3c5c3a38a20 100644 --- a/paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc @@ -12,299 +12,265 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include +#include #include "paddle/fluid/framework/tensor.h" #include "paddle/fluid/operators/fc_op.h" #include "paddle/fluid/platform/device_context.h" #include "paddle/fluid/platform/mkldnn_helper.h" +#include "paddle/fluid/platform/variant.h" namespace paddle { namespace operators { -using paddle::framework::Tensor; -using paddle::platform::MKLDNNDeviceContext; +using framework::DataLayout; +using framework::Tensor; +using framework::LoDTensor; +using framework::DDim; +using framework::ExecutionContext; +using platform::MKLDNNDeviceContext; +using platform::to_void_cast; +using platform::GetMKLDNNFormat; +using mkldnn::memory; +using mkldnn::inner_product_forward; +using mkldnn::primitive; +using mkldnn::stream; +using mkldnn::prop_kind; template -class MKLDNNMD { +class FCPrimitiveFactory { public: - explicit MKLDNNMD(const T* in, const T* w, bool bias) - : in(paddle::framework::vectorize2int(in->dims())), - w(paddle::framework::vectorize2int(w->dims())) { - with_bias_ = bias; - } + explicit FCPrimitiveFactory(const mkldnn::engine& engine) : engine_(engine) {} + + inner_product_forward CreateFcPrimitive(const LoDTensor* input, + const Tensor* weights, + const Tensor* bias, LoDTensor* output, + const ExecutionContext& ctx) { + RecomputeOutputDims(ctx, input, weights, output); + if (fc_) { + UpdateDataPointers(ctx, output, input); + return *fc_; + } + auto src_desc = CreateMemDescriptor(input, input->format()); + input_ = CreateMemory(src_desc, input); - mkldnn::memory::desc dst() const { - return platform::MKLDNNMemDesc({in[0], w[1]}, - mkldnn::memory::data_type::f32, - mkldnn::memory::format::nc); - } + weights_ = TransposeWeights(weights); + if (src_desc.data.ndims == 4) { + weights_ = CreateFourDimWeightsMemory(input, weights); + } + + auto dst_desc = CreateMemDescriptor(output, memory::format::any); - mkldnn::memory::desc src() const { - return is_spatial() - ? platform::MKLDNNMemDesc({in[0], in[1], in[2], in[3]}, - mkldnn::memory::data_type::f32, - mkldnn::memory::format::nchw) - : platform::MKLDNNMemDesc({in[0], in[1]}, - mkldnn::memory::data_type::f32, - mkldnn::memory::format::nc); + fc_ = CreateFcPrimitive(*input_, *weights_, dst_desc, bias, output, ctx); + return *fc_; } - mkldnn::memory::desc weights() const { - return is_spatial() - ? platform::MKLDNNMemDesc({w[1], in[1], in[2], in[3]}, - mkldnn::memory::data_type::f32, - mkldnn::memory::format::oihw) - : platform::MKLDNNMemDesc({w[1], in[1]}, - mkldnn::memory::data_type::f32, - mkldnn::memory::format::oi); + private: + void UpdateDataPointers(const ExecutionContext& ctx, Tensor* out, + const Tensor* in) { + input_->set_data_handle(const_cast(in->data())); + output_->set_data_handle(out->mutable_data(ctx.GetPlace())); + if (out->format() == memory::format::format_undef) { + auto output_format = output_->get_primitive_desc().desc().data.format; + out->set_format((memory::format)output_format); + } } - mkldnn::memory::desc bias() const { - return with_bias_ - ? platform::MKLDNNMemDesc({w[1]}, mkldnn::memory::data_type::f32, - mkldnn::memory::format::format_undef) - : platform::MKLDNNMemDesc({}, mkldnn::memory::data_type::f32, - mkldnn::memory::format::format_undef); + memory::format MatchWeightFormat(memory::format fmt) { + using format = memory::format; + switch (fmt) { + case format::nChw16c: + return format::oIhw16i; + case format::nChw8c: + return format::oIhw8i; + case format::nchw: + return format::oihw; + default: + return format::format_undef; + } } - private: - bool is_spatial() const { return in.size() > 1 && w.size() > 1; } + mkldnn::memory Reorder(const memory::desc& src_desc, + const memory::desc& dst_desc, const void* src_data) { + auto src_mem = memory({src_desc, engine_}, const_cast(src_data)); + auto dst_mem = memory({dst_desc, engine_}); - std::vector in; - std::vector w; - bool with_bias_; - bool is_spatial_; -}; + auto reorder = mkldnn::reorder(src_mem, dst_mem); + stream(stream::kind::eager).submit({reorder}).wait(); -class MKLDNNMemory { - public: - MKLDNNMemory(MKLDNNMD* t, const mkldnn::engine& e) - : md_(t), engine_(e) {} - virtual ~MKLDNNMemory() = default; - - template - mkldnn::memory dst(const Output* out) { - return mkldnn::memory({md_->dst(), engine_}, - static_cast(const_cast(out))); + return dst_mem; } - template - mkldnn::memory dst(Output* out) { - return mkldnn::memory({md_->dst(), engine_}, out); + static mkldnn::memory::desc CreateMemDescriptor(const std::vector& dims, + memory::format format) { + return platform::MKLDNNMemDesc(dims, platform::MKLDNNGetDataType(), + format); } - template - mkldnn::memory src(const Input* in) { - return mkldnn::memory({md_->src(), engine_}, - static_cast(const_cast(in))); + static mkldnn::memory::desc CreateMemDescriptor(const Tensor* tensor, + memory::format format) { + auto dims = framework::vectorize2int(tensor->dims()); + return CreateMemDescriptor(dims, format); } - template - mkldnn::memory weights(const Weight* w) { - return mkldnn::memory({md_->weights(), engine_}, - static_cast(const_cast(w))); + mkldnn::memory CreateMemory(const mkldnn::memory::desc& desc, + const Tensor* tensor) { + return CreateMemory(desc, tensor->data()); } - mkldnn::memory bias() { - return mkldnn::memory(mkldnn::memory::primitive_desc(md_->bias(), engine_)); + mkldnn::memory CreateMemory(const mkldnn::memory::desc& desc, + const void* data) { + return memory({desc, engine_}, const_cast(data)); } - private: - MKLDNNMD* md_; - const mkldnn::engine& engine_; -}; - -template -class FCMKLDNNOpKernel : public paddle::framework::OpKernel { - public: - void Compute(const paddle::framework::ExecutionContext& ctx) const override { - PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), - "It must use CPUPlace."); + mkldnn::memory TransposeWeights(const Tensor* weights) { + auto dims = framework::vectorize2int(weights->dims()); + std::swap(dims[0], dims[1]); // Correct output dimensions + auto src_desc = CreateMemDescriptor(dims, memory::format::io); + auto dst_desc = CreateMemDescriptor(dims, memory::format::oi); + return Reorder(src_desc, dst_desc, weights->data()); + } - auto& dev_ctx = ctx.template device_context(); - const auto& mkldnn_engine = dev_ctx.GetEngine(); + inner_product_forward CreateFcPrimitive(const memory& src_memory, + const memory& weights_memory, + const memory::desc& dst_desc, + const Tensor* bias, Tensor* output, + const ExecutionContext& ctx) { + const auto weights_desc = weights_memory.get_primitive_desc().desc(); + const auto src_desc = src_memory.get_primitive_desc().desc(); + if (bias) { + auto bias_desc = CreateMemDescriptor(bias, bias->format()); + bias_ = CreateMemory(bias_desc, bias); + auto fc_prim_desc = + CreateFcPrimDesc(src_desc, weights_desc, bias_desc, dst_desc); + + output_ = CreateDstMemory(fc_prim_desc, ctx, output); + + return inner_product_forward(fc_prim_desc, src_memory, weights_memory, + *bias_, *output_); + } else { + auto fc_prim_desc = CreateFcPrimDesc(src_desc, weights_desc, dst_desc); + + output_ = CreateDstMemory(fc_prim_desc, ctx, output); + + return inner_product_forward(fc_prim_desc, src_memory, weights_memory, + *output_); + } + } - auto input = ctx.Input("Input"); - auto w = ctx.Input("W"); - auto bias = ctx.Input("Bias"); + mkldnn::inner_product_forward::primitive_desc CreateFcPrimDesc( + const mkldnn::memory::desc& input_desc, + const mkldnn::memory::desc& weights_desc, + const mkldnn::memory::desc& bias_desc, + const mkldnn::memory::desc& dst_desc) { + auto fc_desc = + inner_product_forward::desc(prop_kind::forward_scoring, input_desc, + weights_desc, bias_desc, dst_desc); - PADDLE_ENFORCE(input->dims().size() == 2 || input->dims().size() == 4, - "Input must be with 2 or 4 dimensions, i.e. NCHW"); - // TODO(intel friends): the native weight format is io, - // but the mkldnn weight format is oihw, which may need be transposed. - PADDLE_ENFORCE(w->dims().size() == 2 || w->dims().size() == 4, - "Weights must be with 2 or 4 dimensions, i.e. OI or OIHW"); + return inner_product_forward::primitive_desc(fc_desc, engine_); + } - bool with_bias = bias != nullptr; - MKLDNNMD md(input, w, with_bias); + mkldnn::inner_product_forward::primitive_desc CreateFcPrimDesc( + const mkldnn::memory::desc& input_desc, + const mkldnn::memory::desc& weights_desc, + const mkldnn::memory::desc& dst_desc) { + auto fc_desc = inner_product_forward::desc(prop_kind::forward, input_desc, + weights_desc, dst_desc); - std::shared_ptr pd = - FcFwdPrimitiveDesc(md.src(), md.weights(), md.dst(), md.bias(), - with_bias, mkldnn_engine); + return inner_product_forward::primitive_desc(fc_desc, engine_); + } - const std::string key = ctx.op().Output("Out"); - const std::string key_fc_pd = key + "@fc_pd"; + mkldnn::memory CreateFourDimWeightsMemory(const Tensor* input, + const Tensor* weights) { + auto input_dims = framework::vectorize2int(input->dims()); + auto weight_dims = framework::vectorize2int(weights->dims()); + auto dims = {weight_dims[1], input_dims[1], input_dims[2], input_dims[3]}; - dev_ctx.SetBlob(key_fc_pd, pd); + auto dst_format = MatchWeightFormat(input->format()); + auto src_desc = CreateMemDescriptor(dims, memory::format::oihw); + auto dst_desc = CreateMemDescriptor(dims, dst_format); - MKLDNNMemory mem(&md, mkldnn_engine); + return Reorder(src_desc, dst_desc, weights_->get_data_handle()); + } - const T* input_data = input->data(); - const T* w_data = w->data(); + mkldnn::memory CreateDstMemory( + const mkldnn::inner_product_forward::primitive_desc& fc_prim_desc, + const ExecutionContext& ctx, Tensor* output) { + auto dst_prim_desc = fc_prim_desc.dst_primitive_desc(); + auto buffer_size = dst_prim_desc.get_size(); + T* output_data = output->mutable_data(ctx.GetPlace(), buffer_size); + output->set_format((memory::format)dst_prim_desc.desc().data.format); + return memory(dst_prim_desc, to_void_cast(output_data)); + } - auto output = ctx.Output("Out"); + void RecomputeOutputDims(const ExecutionContext& ctx, const LoDTensor* input, + const Tensor* w, LoDTensor* output) { int in_num_col_dims = ctx.Attr("in_num_col_dims"); std::vector output_dims; FCOutputSize(input->dims(), w->dims(), output_dims, in_num_col_dims); output->Resize(framework::make_ddim(output_dims)); output->set_lod(input->lod()); + } - T* output_data = output->mutable_data(ctx.GetPlace()); - - auto dst_memory = mem.dst(output_data); - auto src_memory = mem.src(input_data); - auto weights_memory = mem.weights(w_data); - // TODO(intel friends): bias memory should also be obtain from bias->data() - auto bias_memory = mem.bias(); + private: + const mkldnn::engine& engine_; + boost::optional bias_; + boost::optional input_; + boost::optional output_; + boost::optional weights_; + boost::optional fc_; +}; - auto forward = with_bias ? mkldnn::inner_product_forward( - *pd, src_memory, weights_memory, bias_memory, - dst_memory) - : mkldnn::inner_product_forward( - *pd, src_memory, weights_memory, dst_memory); +static std::string GetHash(const Tensor* input, const Tensor* weights, + const std::string& suffix) { + auto dim2str = [](const DDim& operand_dims) { + std::string str = ""; + for (size_t i = 0; i < operand_dims.size(); ++i) { + str += std::to_string(operand_dims[i]) + "-"; + } + return str; + }; + return std::to_string((unsigned)input->format()) + dim2str(weights->dims()) + + suffix; +} - std::vector pipeline = {forward}; - mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); +template +std::shared_ptr> GetPrimitiveFactory( + const MKLDNNDeviceContext& dev_ctx, const ExecutionContext& ctx, + const Tensor* input, const Tensor* weights, + const mkldnn::engine& mkldnn_engine) { + const std::string key = GetHash(input, weights, ctx.op().Output("Out")); + + auto prim_creator = + std::static_pointer_cast>(dev_ctx.GetBlob(key)); + if (prim_creator == nullptr) { + prim_creator = std::make_shared>(mkldnn_engine); + dev_ctx.SetBlob(key, prim_creator); } - private: - std::unique_ptr - FcFwdPrimitiveDesc(const mkldnn::memory::desc& src, - const mkldnn::memory::desc& weights, - const mkldnn::memory::desc& dst, - const mkldnn::memory::desc& bias, const bool with_bias, - const mkldnn::engine& engine) const { - auto desc = with_bias - ? mkldnn::inner_product_forward::desc( - mkldnn::prop_kind::forward, src, weights, bias, dst) - : mkldnn::inner_product_forward::desc( - mkldnn::prop_kind::forward, src, weights, dst); - - auto pd = new mkldnn::inner_product_forward::primitive_desc(desc, engine); - return std::unique_ptr(pd); - } -}; + return prim_creator; +} template -class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel { +class FCMKLDNNOpKernel : public framework::OpKernel { public: void Compute(const paddle::framework::ExecutionContext& ctx) const override { - PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), "It must use CPUPlace."); - auto& dev_ctx = ctx.template device_context(); const auto& mkldnn_engine = dev_ctx.GetEngine(); - T* input_grad_data = nullptr; - T* w_grad_data = nullptr; - - Tensor* input_grad = ctx.Output(framework::GradVarName("Input")); - Tensor* w_grad = ctx.Output(framework::GradVarName("W")); - - const Tensor* input = ctx.Input("Input"); - const T* input_data = input->data(); - - const Tensor* w = ctx.Input("W"); - const T* w_data = w->data(); - - if (input_grad) { - input_grad->Resize(input->dims()); - input_grad_data = input_grad->mutable_data(ctx.GetPlace()); - } - if (w_grad) { - w_grad->Resize(w->dims()); - w_grad_data = w_grad->mutable_data(ctx.GetPlace()); - } - - const Tensor* out_grad = ctx.Input(framework::GradVarName("Out")); - const T* out_grad_data = out_grad->data(); - + auto input = ctx.Input("Input"); + auto w = ctx.Input("W"); auto bias = ctx.Input("Bias"); - bool with_bias = bias != nullptr; - - MKLDNNMD md(input, w, with_bias); - MKLDNNMemory mem(&md, mkldnn_engine); - - auto dst_memory = mem.dst(out_grad_data); - auto src_memory = mem.src(input_data); - auto weights_memory = mem.weights(w_data); - auto bias_memory = mem.bias(); + auto output = ctx.Output("Out"); - const std::string key = ctx.op().Input("Out"); - const std::string key_fc_pd = key + "@fc_pd"; + auto prim_creator = + GetPrimitiveFactory(dev_ctx, ctx, input, w, mkldnn_engine); + auto fc = prim_creator->CreateFcPrimitive(input, w, bias, output, ctx); + stream(stream::kind::eager).submit({fc}).wait(); - auto pd = - std::static_pointer_cast( - dev_ctx.GetBlob(key_fc_pd)); - - PADDLE_ENFORCE(pd != nullptr, "Fail to find key_fc_pd in device context"); - - if (w_grad) { - auto weights_grad_memory = mem.weights(w_grad_data); - - mkldnn::inner_product_backward_weights::primitive_desc bwd_weight_pd = - FcBwdWeightsPrimitiveDesc(md.src(), md.weights(), md.dst(), md.bias(), - with_bias, *pd, mkldnn_engine); - - auto bwd_weights_prim = mkldnn::inner_product_backward_weights( - bwd_weight_pd, src_memory, dst_memory, weights_grad_memory, - bias_memory); - - std::vector pipeline{bwd_weights_prim}; - mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); - } - - if (input_grad) { - auto src_grad_memory = mem.src(input_grad_data); - - mkldnn::inner_product_backward_data::primitive_desc bwd_data_pd = - FcBwdDataPrimitiveDesc(md.src(), md.weights(), md.dst(), *pd, - mkldnn_engine); - - auto bwd_data_prim = mkldnn::inner_product_backward_data( - bwd_data_pd, dst_memory, weights_memory, src_grad_memory); - - std::vector pipeline{bwd_data_prim}; - mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); - } - } - - private: - mkldnn::inner_product_backward_weights::primitive_desc - FcBwdWeightsPrimitiveDesc( - const mkldnn::memory::desc& src, const mkldnn::memory::desc& diff_weights, - const mkldnn::memory::desc& diff_dst, const mkldnn::memory::desc& bias, - const bool with_bias, - const mkldnn::inner_product_forward::primitive_desc& pd, - const mkldnn::engine& engine) const { - auto bwd_weight_desc = with_bias - ? mkldnn::inner_product_backward_weights::desc( - src, diff_weights, bias, diff_dst) - : mkldnn::inner_product_backward_weights::desc( - src, diff_weights, diff_dst); - - return mkldnn::inner_product_backward_weights::primitive_desc( - bwd_weight_desc, engine, pd); - } - - mkldnn::inner_product_backward_data::primitive_desc FcBwdDataPrimitiveDesc( - const mkldnn::memory::desc& diff_src, const mkldnn::memory::desc& weights, - const mkldnn::memory::desc& diff_dst, - const mkldnn::inner_product_forward::primitive_desc& pd, - const mkldnn::engine& engine) const { - auto bwd_data_desc = - mkldnn::inner_product_backward_data::desc(diff_src, weights, diff_dst); - return mkldnn::inner_product_backward_data::primitive_desc(bwd_data_desc, - engine, pd); + output->set_layout(DataLayout::kMKLDNN); } }; } // namespace operators @@ -312,6 +278,3 @@ class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel { REGISTER_OP_KERNEL(fc, MKLDNN, ::paddle::platform::CPUPlace, paddle::operators::FCMKLDNNOpKernel); - -REGISTER_OP_KERNEL(fc_grad, MKLDNN, ::paddle::platform::CPUPlace, - paddle::operators::FCMKLDNNGradOpKernel); diff --git a/paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc index 5d8e81921157cbdf35f7016741ab45c362b7261f..c635fd11c37aec5fd75dedfa5ba575868a564232 100644 --- a/paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc @@ -36,7 +36,8 @@ std::string CreateKey(const paddle::framework::ExecutionContext& ctx, const std::vector& ksize, const std::vector& strides, const std::vector& paddings, - const memory::data_type& dt, const std::string& suffix) { + const memory::data_type& dt, const memory::format& fmt, + const std::string& suffix) { std::string key; key.reserve(platform::MKLDNNHandler::MaxKeyLength); platform::MKLDNNHandler::AppendKeyDims(&key, input_dims); @@ -45,6 +46,7 @@ std::string CreateKey(const paddle::framework::ExecutionContext& ctx, platform::MKLDNNHandler::AppendKeyVec(&key, strides); platform::MKLDNNHandler::AppendKeyVec(&key, paddings); platform::MKLDNNHandler::AppendKey(&key, std::to_string(dt)); + platform::MKLDNNHandler::AppendKey(&key, std::to_string(fmt)); platform::MKLDNNHandler::AppendKey(&key, suffix); return key; } @@ -115,8 +117,10 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel { mkldnn::memory::data_type dt = paddle::framework::ToMKLDNNDataType(input->type()); - const std::string key = CreateKey(ctx, src_tz, pooling_type, ksize, strides, - paddings, dt, ctx.op().Output("Out")); + auto fmt = input->format(); + const std::string key = + CreateKey(ctx, src_tz, pooling_type, ksize, strides, paddings, dt, fmt, + ctx.op().Output("Out")); const std::string key_pool_p = key + "@pool_p"; const std::string key_pool_pd = key + "@pool_pd"; const std::string key_pool_src_mem_p = key + "@pool_src_mem_p"; @@ -294,9 +298,9 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel { // Get an unique name from "argument" name of "Out" variable // This name will be used as key when referring info from device context - const std::string key = - CreateKey(ctx, diff_src_tz, pooling_type, ksize, strides, paddings, - memory::data_type::f32, ctx.op().Input("Out")); + const std::string key = CreateKey(ctx, diff_src_tz, pooling_type, ksize, + strides, paddings, memory::data_type::f32, + in_x->format(), ctx.op().Input("Out")); const std::string key_pool_bwd_p = key + "@pool_bwd_p"; const std::string key_pool_diff_src_mem_p = key + "@pool_diff_src_mem_p"; const std::string key_pool_diff_dst_mem_p = key + "@pool_diff_dst_mem_p"; diff --git a/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc index 1b3f33d345f4e0fafd7ad5da41eec052ac2dc504..a01dd512a378217df6f528665a46d50f319e16f7 100644 --- a/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc @@ -54,18 +54,24 @@ class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler { std::shared_ptr AcquireSoftmaxPrimitiveDescriptor(const softmax_forward::desc& softmax_desc, const mkldnn::engine& engine) { - const std::string key_softmax_pd = key_ + "@softmax_pd"; + // Softmax PD has to be passed to Grad op that + // may be executed by diffrent thread, hence + // for that one we use key that does not contain TID + const std::string key_softmax_pd = key_common_ + "@softmax_pd"; - auto softmax_pd = std::static_pointer_cast( + softmax_pd_ = std::static_pointer_cast( dev_ctx_.GetBlob(key_softmax_pd)); - - if (softmax_pd == nullptr) { - softmax_pd_.reset( - new softmax_forward::primitive_desc(softmax_desc, engine)); - dev_ctx_.SetBlob(key_softmax_pd, softmax_pd_); - } else { - softmax_pd_ = softmax_pd; - is_reusing_ = true; + if (softmax_pd_ == nullptr) { + static std::mutex acquire_barrier; + std::lock_guard block_threads_until_finish_this_job( + acquire_barrier); + softmax_pd_ = std::static_pointer_cast( + dev_ctx_.GetBlob(key_softmax_pd)); + if (softmax_pd_ == nullptr) { + softmax_pd_.reset( + new softmax_forward::primitive_desc(softmax_desc, engine)); + dev_ctx_.SetBlob(key_softmax_pd, softmax_pd_); + } } return softmax_pd_; @@ -79,15 +85,11 @@ class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler { auto softmax_p = std::static_pointer_cast( dev_ctx_.GetBlob(prim_key)); - PADDLE_ENFORCE((softmax_p != nullptr) || (is_reusing_ == false), - "Fail to find softmax primitive in device context"); if (softmax_p == nullptr) { softmax_p = std::make_shared( *softmax_pd_, *(static_cast(src_memory_p.get())), *(static_cast(dst_memory_p.get()))); dev_ctx_.SetBlob(prim_key, softmax_p); - } else { - is_reusing_ = true; } return softmax_p; @@ -100,15 +102,11 @@ class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler { auto prim_key = key_ + "@softmax_bwd_p"; auto softmax_bwd_p = std::static_pointer_cast( dev_ctx_.GetBlob(prim_key)); - PADDLE_ENFORCE((softmax_bwd_p != nullptr) || (is_reusing_ == false), - "Fail to find softmax backward primitive in device context"); if (softmax_bwd_p == nullptr) { softmax_bwd_p = std::make_shared( *softmax_bwd_pd_, *dst_memory_p, *diff_dst_memory_p, *diff_src_memory_p); dev_ctx_.SetBlob(prim_key, softmax_bwd_p); - } else { - is_reusing_ = true; } return softmax_bwd_p; diff --git a/paddle/fluid/operators/mkldnn/transpose_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/transpose_mkldnn_op.cc index 95cee806ac451235a8fb03567e6057e10aa56427..480167f43525bc19defab0faab31460e6c179eff 100644 --- a/paddle/fluid/operators/mkldnn/transpose_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/transpose_mkldnn_op.cc @@ -46,7 +46,8 @@ class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel { std::vector nchw_tz = paddle::framework::vectorize2int(input->dims()); const std::string key = platform::TransposeMKLDNNHandler::GetHash( - nchw_tz, axis, ctx.op().Output("Out")); + nchw_tz, axis, + ctx.op().Output("Out") + std::to_string(input->format())); platform::TransposeMKLDNNHandler handler(nchw_tz, axis, dev_ctx, mkldnn_engine, key); diff --git a/paddle/fluid/operators/mul_op.cc b/paddle/fluid/operators/mul_op.cc index 05afdf53240484212901febee431cef2b35bb75c..bbf9fbfa1ff33d210417da750b9ab3813baf64e1 100644 --- a/paddle/fluid/operators/mul_op.cc +++ b/paddle/fluid/operators/mul_op.cc @@ -13,7 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/mul_op.h" +#include #include +#include #include namespace paddle { @@ -178,16 +180,72 @@ class MulOpGradMaker : public framework::SingleGradOpDescMaker { } }; +class MulDoubleGradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); + PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null"); + PADDLE_ENFORCE(ctx->HasInput("DOut"), "Input(DOut) should not be null"); + + if (ctx->HasOutput("DDOut") && ctx->HasInput("DDX")) { + ctx->ShareDim("DOut", "DDOut"); + } + if (ctx->HasOutput("DX") && ctx->HasInput("DDY")) { + ctx->ShareDim("X", "DX"); + } + if (ctx->HasOutput("DY") && ctx->HasInput("DDX")) { + ctx->ShareDim("Y", "DY"); + } + } +}; + +class MulDoubleGradMaker : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + std::unique_ptr retv(new framework::OpDesc()); + retv->SetType("mul_grad_grad"); + + retv->SetInput("X", Input("X")); + retv->SetInput("Y", Input("Y")); + retv->SetInput("DOut", Input(framework::GradVarName("Out"))); + retv->SetInput("DDX", OutputGrad(framework::GradVarName("X"))); + retv->SetInput("DDY", OutputGrad(framework::GradVarName("Y"))); + + auto ddx = OutputGrad(framework::GradVarName("X")); + auto ddw = OutputGrad(framework::GradVarName("Y")); + std::vector empty_str = {}; + + retv->SetOutput("DDOut", (ddx.empty()) + ? empty_str + : InputGrad(framework::GradVarName("Out"))); + retv->SetOutput("DX", ddw.empty() ? empty_str : InputGrad("X")); + retv->SetOutput("DY", ddx.empty() ? empty_str : InputGrad("Y")); + + retv->SetAttrMap(Attrs()); + return retv; + } +}; + } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OPERATOR(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpInferVarType, ops::MulOpGradMaker); -REGISTER_OPERATOR(mul_grad, ops::MulGradOp); +REGISTER_OPERATOR(mul_grad, ops::MulGradOp, ops::MulDoubleGradMaker); +REGISTER_OPERATOR(mul_grad_grad, ops::MulDoubleGradOp); REGISTER_OP_CPU_KERNEL( mul, ops::MulKernel, ops::MulKernel); REGISTER_OP_CPU_KERNEL( mul_grad, ops::MulGradKernel, ops::MulGradKernel); +REGISTER_OP_CPU_KERNEL( + mul_grad_grad, + ops::MulDoubleGradKernel, + ops::MulDoubleGradKernel); diff --git a/paddle/fluid/operators/mul_op.cu.cc b/paddle/fluid/operators/mul_op.cu.cc index 6c5a83c6a50c463502171f09bbf18e17e43917b5..6e841712b9bffc06ca56afddcb866af8b3f9b0d8 100644 --- a/paddle/fluid/operators/mul_op.cu.cc +++ b/paddle/fluid/operators/mul_op.cu.cc @@ -24,3 +24,7 @@ REGISTER_OP_CUDA_KERNEL( mul_grad, ops::MulGradKernel, ops::MulGradKernel, ops::MulGradKernel); +REGISTER_OP_CUDA_KERNEL( + mul_grad_grad, + ops::MulDoubleGradKernel, + ops::MulDoubleGradKernel); diff --git a/paddle/fluid/operators/mul_op.h b/paddle/fluid/operators/mul_op.h index f72824806ed6ee3a4490938403d441326f8a3d4a..c77eb5c4ccbff1900e705bf51f5c5fc9096aa4eb 100644 --- a/paddle/fluid/operators/mul_op.h +++ b/paddle/fluid/operators/mul_op.h @@ -15,6 +15,7 @@ limitations under the License. */ #pragma once #include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/framework/operator.h" #include "paddle/fluid/operators/math/blas.h" #include "paddle/fluid/operators/math/math_function.h" @@ -109,5 +110,95 @@ class MulGradKernel : public framework::OpKernel { } }; +template +class MulDoubleGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + int x_num_col_dims = ctx.template Attr("x_num_col_dims"); + int y_num_col_dims = ctx.template Attr("y_num_col_dims"); + auto* x = ctx.Input("X"); + auto* y = ctx.Input("Y"); + auto x_mat = x->dims().size() > 2 + ? framework::ReshapeToMatrix(*x, x_num_col_dims) + : static_cast(*x); + auto y_mat = y->dims().size() > 2 + ? framework::ReshapeToMatrix(*y, y_num_col_dims) + : static_cast(*y); + + const int m = framework::flatten_to_2d(x->dims(), x_num_col_dims)[0]; + const int n = framework::flatten_to_2d(y->dims(), y_num_col_dims)[1]; + + auto* dout = ctx.Input("DOut"); + Tensor dout_mat; + dout_mat.ShareDataWith(*dout); + dout_mat.Resize({m, n}); + + auto* ddx = ctx.Input("DDX"); + auto* ddy = ctx.Input("DDY"); + + auto* dx = ctx.Output("DX"); + auto* dy = ctx.Output("DY"); + auto* ddout = ctx.Output("DDOut"); + + Tensor ddout_mat; + if (ddout) { + ddout->set_lod(dout->lod()); + // allocate and reshape ddout + ddout->mutable_data(ctx.GetPlace()); + ddout_mat.ShareDataWith(*ddout); + ddout_mat.Resize({m, n}); + } + + auto& dev_ctx = ctx.template device_context(); + auto blas = math::GetBlas(dev_ctx); + // a flag to specify whether ddout value has been set, if flag + // is false, MatMul beta should be 0 to set ddout, if flag is + // true, MatMul beta should be 1 to add result to ddout. + bool ddout_flag = false; + if (ddx) { + auto ddx_mat = ddx->dims().size() > 2 + ? framework::ReshapeToMatrix(*ddx, x_num_col_dims) + : static_cast(*ddx); + + // dy = ddx' * dout. dy : K x M, ddx' : K x M, dout : M x N + if (dy) { + dy->set_lod(y->lod()); + // allocate and reshape dy + dy->mutable_data(ctx.GetPlace()); + Tensor dy_mat = dy->dims().size() > 2 + ? framework::ReshapeToMatrix(*dy, y_num_col_dims) + : *dy; + blas.MatMul(ddx_mat, true, dout_mat, false, &dy_mat); + } + // ddout1 = ddx * y. ddx : M x K, y : K x N, ddout1 : M x N + if (ddout) { + blas.MatMul(ddx_mat, false, y_mat, false, static_cast(1.0), + &ddout_mat, static_cast(ddout_flag)); + ddout_flag = true; + } + } + if (ddy) { + auto ddy_mat = ddy->dims().size() > 2 + ? framework::ReshapeToMatrix(*ddy, y_num_col_dims) + : static_cast(*ddy); + // dx = dout * ddy'. dout : M x N, ddy' : N x K, dx : M x K + if (dx) { + dx->set_lod(x->lod()); + // allocate and reshape dx + dx->mutable_data(ctx.GetPlace()); + Tensor dx_mat = dx->dims().size() > 2 + ? framework::ReshapeToMatrix(*dx, x_num_col_dims) + : *dx; + blas.MatMul(dout_mat, false, ddy_mat, true, &dx_mat); + } + // ddout2 = x * ddy. x : M x K, ddy : K x N, ddout2 : M x N + if (ddout) { + blas.MatMul(x_mat, false, ddy_mat, false, static_cast(1.0), + &ddout_mat, static_cast(ddout_flag)); + } + } + } +}; + } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/ngraph/ngraph_bridge.cc b/paddle/fluid/operators/ngraph/ngraph_bridge.cc index dafc31b546e3ca6d8dc8d5634dd51cff9fe5bfb7..db8a7ca94a557d1d93b7dc73b2eee4a36d3783e3 100644 --- a/paddle/fluid/operators/ngraph/ngraph_bridge.cc +++ b/paddle/fluid/operators/ngraph/ngraph_bridge.cc @@ -15,6 +15,7 @@ limitations under the License. */ #include #include #include +#include #include #include "ngraph/ngraph.hpp" @@ -24,6 +25,8 @@ limitations under the License. */ #include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/ngraph_helper.h" +constexpr int64_t kNoPadding = -1; + namespace paddle { namespace operators { @@ -31,6 +34,34 @@ bool NgraphBridge::isRegister(const std::string& str) { return ops::NgraphSingleton::Lookup(str); } +bool NgraphBridge::isSupported( + const std::unique_ptr& op) { + static std::unordered_set skip_op_list{ + "reshape", "reshape2", "lookup_table", "lookup_table_grad"}; + bool result = true; + auto& op_type = op->Type(); + auto op_attrs = paddle::framework::AttrReader(op->Attrs()); + if (!isRegister(op_type)) { + if (skip_op_list.count(op_type)) { + if (op_type == "lookup_table" || op_type == "lookup_table_grad") { + if (op_attrs.Get("is_sparse") || + (op_attrs.Get("padding_idx") != kNoPadding)) { + result = false; + } + } else if ((op_type == "reshape") || (op_type == "reshape2")) { + if (op->Input("Shape") != paddle::framework::kEmptyVarName) { + result = false; + } + } else { + result = false; + } + } + } else { + result = false; + } + return result; +} + void NgraphBridge::BuildNgNode( const std::shared_ptr& op) { auto& op_type = op->Type(); diff --git a/paddle/fluid/operators/ngraph/ngraph_bridge.h b/paddle/fluid/operators/ngraph/ngraph_bridge.h index b609c284959238689eaf35c87d1bc4e4330b5c1f..0b43ec53874d962699abef3cf843c5518d6f072d 100644 --- a/paddle/fluid/operators/ngraph/ngraph_bridge.h +++ b/paddle/fluid/operators/ngraph/ngraph_bridge.h @@ -39,6 +39,8 @@ class NgraphBridge { static bool isRegister(const std::string& str); + static bool isSupported(const std::unique_ptr& op); + private: std::shared_ptr< std::unordered_map>> diff --git a/paddle/fluid/operators/ngraph/ngraph_engine.cc b/paddle/fluid/operators/ngraph/ngraph_engine.cc index 5ef385d2fcbaf01dce5c9b85321b41c103e5655a..ae87687e34202bc718903748b0c5042ba3eefb3f 100644 --- a/paddle/fluid/operators/ngraph/ngraph_engine.cc +++ b/paddle/fluid/operators/ngraph/ngraph_engine.cc @@ -38,6 +38,10 @@ namespace operators { static ngraph::Shape Ddim2Shape(const framework::DDim& dims) { ngraph::Shape sp; + if (dims.size() == 1 && dims[0] == 0) { + sp.emplace_back(0); + return sp; + } for (int i = 0; i < dims.size(); ++i) { int k = dims[i]; k = k == 0 ? 1 : k; @@ -61,6 +65,7 @@ static std::map {framework::proto::VarType::FP64, ngraph::element::f64}, {framework::proto::VarType::INT32, ngraph::element::i32}, {framework::proto::VarType::INT64, ngraph::element::i64}, + {framework::proto::VarType::UINT8, ngraph::element::u8}, {framework::proto::VarType::BOOL, ngraph::element::boolean}}; static std::map @@ -69,6 +74,7 @@ static std::map {ngraph::element::f64, framework::proto::VarType::FP64}, {ngraph::element::i32, framework::proto::VarType::INT32}, {ngraph::element::i64, framework::proto::VarType::INT64}, + {ngraph::element::u8, framework::proto::VarType::UINT8}, {ngraph::element::boolean, framework::proto::VarType::BOOL}}; std::vector NgraphEngine::feed_vars = {}; @@ -132,12 +138,11 @@ static std::vector> NgraphOpIntervals( int pivot = left; while (pivot < right) { auto op_type = ops->at(pivot)->Type(); - if (NgraphBridge::isRegister(op_type)) { + if (!NgraphBridge::isSupported(ops->at(pivot))) { ++pivot; } else { int start = pivot, end = start; - while (pivot < right && - (!NgraphBridge::isRegister(ops->at(pivot)->Type()))) { + while (pivot < right && (NgraphBridge::isSupported(ops->at(pivot)))) { ++pivot; ++end; } @@ -156,6 +161,8 @@ static void SubstituteNgraphOp( ng_op_desc.SetAttr("interval", interval); ng_op_desc.SetAttr("engine_key", engine_key); ng_op_desc.SetAttr("graph", block_str); + ng_op_desc.SetInput("Xs", std::vector(0)); + ng_op_desc.SetOutput("Ys", std::vector(0)); ops->erase(ops->begin() + interval[0], ops->begin() + interval[1]); ops->insert(ops->begin() + interval[0], @@ -221,20 +228,36 @@ NgraphEngine::NgraphEngine(const framework::Scope& scope, const platform::Place& place, const framework::ExecutionContext& ctx) : scope_(scope), place_(place) { - std::string serialized_graph = ctx.Attr("graph"); - auto interval = ctx.Attr>("interval"); - std::string engine_key = ctx.Attr("engine_key"); - var_in_node_map_ = std::make_shared< std::unordered_map>>(); var_node_map_ = std::make_shared< std::unordered_map>>(); - GetNgFunction(engine_key, interval); + GetNgFunction(ctx); } -void NgraphEngine::Prepare(const std::vector& interval) { +void NgraphEngine::Prepare(const framework::ExecutionContext& ctx) { + auto interval = ctx.Attr>("interval"); + std::string serialized_graph = ctx.Attr("graph"); + + auto input_vars = ctx.Inputs("Xs"); + if (!input_vars.empty()) { + feed_vars = input_vars; + var_in_ = input_vars; + } + auto output_vars = ctx.Outputs("Ys"); + if (!output_vars.empty()) { + var_out_ = output_vars; + } + + framework::proto::BlockDesc block_proto; + if (!serialized_graph.empty()) block_proto.ParseFromString(serialized_graph); + framework::BlockDesc block_desc(nullptr, &block_proto); + if (!serialized_graph.empty()) { + NgraphEngine::p_bdesc = &block_desc; + } + bool has_fetch = false, is_full = false; for (auto& var : p_bdesc->AllVars()) { if (!(var->GetType() == framework::proto::VarType::SELECTED_ROWS || @@ -314,7 +337,15 @@ void NgraphEngine::Prepare(const std::vector& interval) { op_state_ = OpState::UNKNOWN; } - BuildNgIO(ops_desc, interval); + if (var_in_.empty() && var_out_.empty()) { + BuildNgIO(ops_desc, interval); + } + for (size_t i = 0; i < var_in_.size(); ++i) { + auto var_name = var_in_[i]; + if (persistables_.find(var_name) == persistables_.end()) { + var_in_updates_.emplace_back(i); + } + } } void NgraphEngine::BuildNgIO(const std::vector& ops_desc, @@ -390,12 +421,14 @@ void NgraphEngine::BuildNgIO(const std::vector& ops_desc, } } } - - for (size_t i = 0; i < var_in_.size(); ++i) { - auto var_name = var_in_[i]; - if (persistables_.find(var_name) == persistables_.end()) { - var_in_updates_.emplace_back(i); + // remove output duplicates + std::unordered_set var_out_set; + for (int i = static_cast(var_out_.size()) - 1; i >= 0; --i) { + std::string var_name = var_out_.at(i); + if (var_out_set.count(var_name)) { + var_out_.erase(var_out_.begin() + i); } + var_out_set.insert(var_name); } } @@ -432,26 +465,17 @@ void NgraphEngine::BuildNgNodes() { } } } - NgraphBridge ngb(var_node_map_); for (auto& op : fused_ops_) { ngb.BuildNgNode(op); } } -void NgraphEngine::RunInferShape() { - for (auto& op : fused_ops_) { - framework::RuntimeContext ctx(op->Inputs(), op->Outputs(), scope_); - op->RuntimeInferShape(scope_, place_, ctx); - } -} - -void NgraphEngine::BuildNgFunction(const std::vector& interval) { - Prepare(interval); - RunInferShape(); +std::shared_ptr NgraphEngine::BuildNgFunction( + const framework::ExecutionContext& ctx) { + Prepare(ctx); GetNgInputShape(); BuildNgNodes(); - ngraph_function_ = nullptr; ngraph::NodeVector func_outputs; ngraph::ParameterVector func_inputs; @@ -466,93 +490,105 @@ void NgraphEngine::BuildNgFunction(const std::vector& interval) { func_inputs.emplace_back(prm); } - ngraph_function_ = - std::make_shared(func_outputs, func_inputs); + return std::make_shared(func_outputs, func_inputs); +} + +void NgraphEngine::ClearNgCache() { + auto it = engine_cache.begin(); + while (it != engine_cache.end()) { + auto ng_engine = it->second; + backend_->remove_compiled_function(ng_engine.ngraph_handle); + ++it; + } + engine_cache.clear(); + auto it_tensor = t_in_cache_.begin(); + while (it_tensor != t_in_cache_.end()) { + auto t_vec = it_tensor->second; + for (auto t_in : t_vec) { + t_in.reset(); + } + ++it_tensor; + } + t_in_cache_.clear(); } -void NgraphEngine::GetNgFunction(std::string engine_key, - const std::vector& interval) { +void NgraphEngine::GetNgFunction(const framework::ExecutionContext& ctx) { + auto interval = ctx.Attr>("interval"); + std::string engine_key = ctx.Attr("engine_key"); + + // set to flase, to debug cache or recompile everytime. bool use_cache = true; - if (use_cache) { - this->func_cache_key_ = ""; - for (int i = 0; i < std::min(static_cast(feed_vars.size()), 10); ++i) { - auto* var = scope_.FindVar(feed_vars[i]); - if (var && var->IsType()) { - auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var); - auto dims = tensor_pd->dims(); - for (int j = 0; j < dims.size(); ++j) { - func_cache_key_ += std::to_string(dims[j]); - } + if (!use_cache) ClearNgCache(); + + this->func_cache_key_ = ""; + for (int i = 0; i < static_cast(feed_vars.size()); ++i) { + auto* var = scope_.FindVar(feed_vars[i]); + if (var && var->IsType()) { + auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var); + auto dims = tensor_pd->dims(); + for (int j = 0; j < dims.size(); ++j) { + func_cache_key_ += std::to_string(dims[j]); } } - func_cache_key_ += std::to_string(interval[0]) + "_" + - std::to_string(interval[1]) + engine_key; - func_cache_key_ = std::to_string(std::hash()(func_cache_key_)); - - if (engine_cache.find(func_cache_key_) != engine_cache.end()) { - if (engine_cache[func_cache_key_].persistables.size() == 0) { - engine_cache.clear(); - t_in_cache_.clear(); - } else { - auto var_name = engine_cache[func_cache_key_].persistables.begin(); - framework::Variable* var = scope_.FindVar(*var_name); - if (var != pre_var_ptr) { - engine_cache.clear(); - t_in_cache_.clear(); - } - pre_var_ptr = var; + } + func_cache_key_ += std::to_string(interval[0]) + "_" + + std::to_string(interval[1]) + engine_key; + func_cache_key_ = std::to_string(std::hash()(func_cache_key_)); + + if (engine_cache.find(func_cache_key_) != engine_cache.end()) { + if (engine_cache[func_cache_key_].persistables.size() == 0) { + ClearNgCache(); + } else { + auto var_name = engine_cache[func_cache_key_].persistables.begin(); + framework::Variable* var = scope_.FindVar(*var_name); + if (var != pre_var_ptr) { + ClearNgCache(); } + pre_var_ptr = var; } + } - if (engine_cache.find(func_cache_key_) == engine_cache.end()) { - BuildNgFunction(interval); - engine_cache[func_cache_key_].ngraph_function = this->ngraph_function_; - engine_cache[func_cache_key_].persistables = this->persistables_; - engine_cache[func_cache_key_].var_in_updates = this->var_in_updates_; - engine_cache[func_cache_key_].var_in = this->var_in_; - engine_cache[func_cache_key_].var_out = this->var_out_; - engine_cache[func_cache_key_].is_test = this->is_test_; + if (engine_cache.find(func_cache_key_) == engine_cache.end()) { + if (engine_cache.size() > 5) ClearNgCache(); + auto func = BuildNgFunction(ctx); + // Due to optimization backend may produce results in other layouts, + // make sure we get default layout for results. + for (auto& r : func->get_results()) { + r->set_needs_default_layout(true); } - } else { - BuildNgFunction(interval); + engine_cache[func_cache_key_].ngraph_handle = backend_->compile(func); + engine_cache[func_cache_key_].persistables = this->persistables_; + engine_cache[func_cache_key_].var_in_updates = this->var_in_updates_; + engine_cache[func_cache_key_].var_in = this->var_in_; + engine_cache[func_cache_key_].var_out = this->var_out_; + engine_cache[func_cache_key_].is_test = this->is_test_; } } void NgraphEngine::Run(const framework::Scope& scope, const platform::Place& place) const { - std::shared_ptr ng_func; + std::shared_ptr ng_handle; const std::set* p_persistables; const std::vector* p_var_in_updates; const std::vector* p_var_in; const std::vector* p_var_out; bool is_test; - bool use_cache = true; - if (use_cache) { - PADDLE_ENFORCE(engine_cache.find(func_cache_key_) != engine_cache.end(), - "Cannot find cached data to run ngraph function"); - ng_func = engine_cache[func_cache_key_].ngraph_function; - p_persistables = &(engine_cache[func_cache_key_].persistables); - p_var_in_updates = &(engine_cache[func_cache_key_].var_in_updates); - p_var_in = &(engine_cache[func_cache_key_].var_in); - p_var_out = &(engine_cache[func_cache_key_].var_out); - is_test = engine_cache[func_cache_key_].is_test; - } else { - ng_func = ngraph_function_; - p_persistables = &this->persistables_; - p_var_in_updates = &this->var_in_updates_; - p_var_in = &this->var_in_; - p_var_out = &this->var_out_; - is_test = this->is_test_; - } + PADDLE_ENFORCE(engine_cache.find(func_cache_key_) != engine_cache.end(), + "Cannot find cached data to run ngraph function"); + ng_handle = engine_cache[func_cache_key_].ngraph_handle; + p_persistables = &(engine_cache[func_cache_key_].persistables); + p_var_in_updates = &(engine_cache[func_cache_key_].var_in_updates); + p_var_in = &(engine_cache[func_cache_key_].var_in); + p_var_out = &(engine_cache[func_cache_key_].var_out); + is_test = engine_cache[func_cache_key_].is_test; std::vector>* p_t_in; std::vector> t_in = {}; - auto m_parameters = ng_func->get_parameters(); - auto m_results = ng_func->get_results(); - if (is_test && use_cache && - t_in_cache_.find(func_cache_key_) != t_in_cache_.end()) { + auto m_parameters = ng_handle->get_parameters(); + auto m_results = ng_handle->get_results(); + if (is_test && t_in_cache_.find(func_cache_key_) != t_in_cache_.end()) { p_t_in = &(t_in_cache_[func_cache_key_]); for (size_t i = 0; i < p_var_in_updates->size(); ++i) { int index = p_var_in_updates->at(i); @@ -571,7 +607,7 @@ void NgraphEngine::Run(const framework::Scope& scope, } } } else { - if (is_test && use_cache) { + if (is_test) { p_t_in = &(t_in_cache_[func_cache_key_]); } else { p_t_in = &t_in; @@ -601,6 +637,21 @@ void NgraphEngine::Run(const framework::Scope& scope, } } + for (auto& op : fused_ops_) { + framework::RuntimeContext ctx(op->Inputs(), op->Outputs(), scope_); + if (op->Type() == "reshape2_grad") { + auto xshape_name = op->Inputs().at("XShape").at(0); + auto* xshape_var = scope_.FindVar(xshape_name); + auto* xshape_tensor = GetLoDTensorOrSelectedRowsValueFromVar(*xshape_var); + auto& xshape_ddim = xshape_tensor->dims(); + auto xgrad_name = op->Outputs().at(framework::GradVarName("X")).at(0); + auto* xgrad_var = scope_.FindVar(xgrad_name); + xgrad_var->GetMutable()->Resize(xshape_ddim); + } else { + op->RuntimeInferShape(scope_, place_, ctx); + } + } + std::vector> t_out = {}; for (size_t i = 0; i < p_var_out->size(); ++i) { auto vo = p_var_out->at(i); @@ -619,8 +670,7 @@ void NgraphEngine::Run(const framework::Scope& scope, } } - auto handle = backend_->compile(ng_func); - handle->call_with_validate(t_out, *p_t_in); + ng_handle->call(t_out, *p_t_in); } // NgraphEngine::Run } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/ngraph/ngraph_engine.h b/paddle/fluid/operators/ngraph/ngraph_engine.h index 19400ac5b0ecd9d3254583b8db9889fc6cf8bc0f..4cb1465371356cd1ef76113fc4e78d7f1e188746 100644 --- a/paddle/fluid/operators/ngraph/ngraph_engine.h +++ b/paddle/fluid/operators/ngraph/ngraph_engine.h @@ -40,7 +40,7 @@ enum class OpState { /* nGraph support state on ops */ // cache engine repetitives struct EngineCache { - std::shared_ptr ngraph_function; + std::shared_ptr ngraph_handle; std::set persistables; std::vector var_in; std::vector var_out; @@ -84,8 +84,6 @@ class NgraphEngine { // ngraph backend eg. CPU static std::shared_ptr backend_; - // ngraph function to call and execute - std::shared_ptr ngraph_function_; // var_name of inputs std::vector var_in_; // var_name of outputs from fetch in order @@ -101,7 +99,7 @@ class NgraphEngine { std::unordered_map>> var_node_map_; // prepare info for ngraph engine need - void Prepare(const std::vector& interval); + void Prepare(const framework::ExecutionContext& ctx); // get ngraph engine input and output list void BuildNgIO(const std::vector& op_descs, const std::vector& interval); @@ -109,12 +107,13 @@ class NgraphEngine { void GetNgInputShape(); // Call ngraph bridge to map ops void BuildNgNodes(); - // run paddle RuntimeInferShape to get the tensor shape - void RunInferShape(); // build ngraph function call - void BuildNgFunction(const std::vector& interval); + std::shared_ptr BuildNgFunction( + const framework::ExecutionContext& ctx); + // clear ngraph engine cache and t_in cache + void ClearNgCache(); // Check cache for ngraph function or otherwise build the function - void GetNgFunction(std::string engine_key, const std::vector& interval); + void GetNgFunction(const framework::ExecutionContext& ctx); }; } // namespace operators diff --git a/paddle/fluid/operators/ngraph/ops/activation_op.h b/paddle/fluid/operators/ngraph/ops/activation_op.h index a66ec65a336f807f554157628888633db22ebfec..884ec659267a5386b5715b9f8b38be8900123823 100644 --- a/paddle/fluid/operators/ngraph/ops/activation_op.h +++ b/paddle/fluid/operators/ngraph/ops/activation_op.h @@ -26,6 +26,52 @@ namespace paddle { namespace operators { namespace ngraphs { +void BuildGeluNode( + const std::shared_ptr& op, + std::shared_ptr< + std::unordered_map>> + ngb_node_map) { + auto input = platform::GetInputNode(op, "X", ngb_node_map); + auto half = paddle::platform::CreateConstant(input->get_element_type(), + input->get_shape(), {0.5}); + auto one = paddle::platform::CreateConstant(input->get_element_type(), + input->get_shape(), {1}); + auto sqrt_two = + std::make_shared(paddle::platform::CreateConstant( + input->get_element_type(), input->get_shape(), {2})); + auto out = half * input * + (one + std::make_shared(input / sqrt_two)); + platform::SetOutputNode(op, "Out", out, ngb_node_map); +} + +void BuildGeluGradNode( + const std::shared_ptr& op, + std::shared_ptr< + std::unordered_map>> + ngb_node_map) { + auto input = platform::GetInputNode(op, "X", ngb_node_map); + auto dout = platform::GetInputNode(op, "Out@GRAD", ngb_node_map); + auto half = paddle::platform::CreateConstant(input->get_element_type(), + input->get_shape(), {0.5}); + auto minus_half = paddle::platform::CreateConstant( + input->get_element_type(), input->get_shape(), {-0.5}); + auto one = paddle::platform::CreateConstant(input->get_element_type(), + input->get_shape(), {1}); + auto two = paddle::platform::CreateConstant(input->get_element_type(), + input->get_shape(), {2}); + auto pi = paddle::platform::CreateConstant( + input->get_element_type(), input->get_shape(), {3.14159265359}); + auto sqrt_two = std::make_shared(two); + auto sqrt_pi = std::make_shared(pi); + + auto first = + half * (one + std::make_shared(input * one / sqrt_two)); + auto second = half * (two / sqrt_pi) * (one / sqrt_two) * input * + std::make_shared(minus_half * input * input); + auto gelu_grad = dout * (first + second); + platform::SetOutputNode(op, "X@GRAD", gelu_grad, ngb_node_map); +} + void BuildReluGradNode( const std::shared_ptr& op, std::shared_ptr< @@ -37,6 +83,16 @@ void BuildReluGradNode( platform::SetOutputNode(op, "X@GRAD", relu_grad, ngb_node_map); } +void BuildSquareNode( + const std::shared_ptr& op, + std::shared_ptr< + std::unordered_map>> + ngb_node_map) { + auto input = platform::GetInputNode(op, "X", ngb_node_map); + auto out = input * input; + platform::SetOutputNode(op, "Out", out, ngb_node_map); +} + void BuildTanhGradNode( const std::shared_ptr& op, std::shared_ptr< @@ -54,5 +110,8 @@ void BuildTanhGradNode( } // namespace operators } // namespace paddle +REGISTER_NG_OP(gelu, BuildGeluNode); +REGISTER_NG_OP(gelu_grad, BuildGeluGradNode); REGISTER_NG_OP(relu_grad, BuildReluGradNode); +REGISTER_NG_OP(square, BuildSquareNode); REGISTER_NG_OP(tanh_grad, BuildTanhGradNode); diff --git a/paddle/fluid/operators/ngraph/ops/binary_unary_op.h b/paddle/fluid/operators/ngraph/ops/binary_unary_op.h index 2d11775849a778262dcd3e36ff35d8851fb350f1..b8e9f3d85847e2441057a1041c55a1046ff15cee 100644 --- a/paddle/fluid/operators/ngraph/ops/binary_unary_op.h +++ b/paddle/fluid/operators/ngraph/ops/binary_unary_op.h @@ -51,6 +51,11 @@ static void BuildUnaryNode( } // namespace operators } // namespace paddle +REGISTER_NG_OP(abs, BuildUnaryNode); REGISTER_NG_OP(relu, BuildUnaryNode); REGISTER_NG_OP(tanh, BuildUnaryNode); REGISTER_NG_OP(sigmoid, BuildUnaryNode); + +REGISTER_NG_OP(logical_and, BuildBinaryNode); +REGISTER_NG_OP(logical_or, BuildBinaryNode); +REGISTER_NG_OP(logical_not, BuildUnaryNode); diff --git a/paddle/fluid/operators/ngraph/ops/conv2d_op.h b/paddle/fluid/operators/ngraph/ops/conv2d_op.h index be766ebeb4796be102c917296238b8ab14710131..b8ad7491d57b2d509c8a30b7848590339b13056b 100644 --- a/paddle/fluid/operators/ngraph/ops/conv2d_op.h +++ b/paddle/fluid/operators/ngraph/ops/conv2d_op.h @@ -239,3 +239,4 @@ void BuildConv2dGradNode( REGISTER_NG_OP(conv2d, BuildConv2dNode); REGISTER_NG_OP(conv2d_grad, BuildConv2dGradNode); +REGISTER_NG_OP(depthwise_conv2d, BuildConv2dNode); diff --git a/paddle/fluid/operators/ngraph/ops/cross_entropy_op.h b/paddle/fluid/operators/ngraph/ops/cross_entropy_op.h index c92ebb7e96fa22f8fd463c5837134cd74542766c..bc91be45325e5aafe5c38cc4979766cedea962e6 100644 --- a/paddle/fluid/operators/ngraph/ops/cross_entropy_op.h +++ b/paddle/fluid/operators/ngraph/ops/cross_entropy_op.h @@ -26,59 +26,82 @@ limitations under the License. */ namespace paddle { namespace operators { namespace ngraphs { +std::shared_ptr remove_trailing_one( + const std::shared_ptr& input) { + auto shape = input->get_shape(); + if (shape.back() == 1) { + shape.pop_back(); + return platform::NgReshaper(input, shape); + } else { + return input; + } +} -std::shared_ptr GetCrossEntropy( - std::shared_ptr x, std::shared_ptr label, - const bool is_soft_label, int ignore_index) { - auto label_shape = label->get_shape(); - auto x_shape = x->get_shape(); - auto label_rank = label_shape.size(); - auto x_rank = x_shape.size(); - std::shared_ptr x_2d = x, label_2d = label; - auto label_2d_shape = label_shape, x_2d_shape = x_shape; - - if (label_rank > 2) { - label_2d_shape = paddle::platform::FlattenTo2d(label_shape, label_rank - 1); - label_2d = paddle::platform::NgReshaper(label, label_2d_shape); +std::shared_ptr flatten_node( + const std::shared_ptr& input) { + auto shape = input->get_shape(); + auto rank = shape.size(); + auto output = input; + if (rank > 2) { + auto shape_2d = paddle::platform::FlattenTo2d(shape, rank - 1); + output = paddle::platform::NgReshaper(input, shape_2d); } - if (x_rank > 2) { - x_2d_shape = platform::FlattenTo2d(x_shape, x_rank - 1); - x_2d = platform::NgReshaper(x, x_2d_shape); + return output; +} + +std::shared_ptr convert_to_node_type( + const std::shared_ptr& input, + const std::shared_ptr& ref) { + auto output = input; + if (input->get_element_type() != ref->get_element_type()) { + output = + std::make_shared(input, ref->get_element_type()); } + return output; +} - auto batch_size = x_2d_shape.at(0); +std::shared_ptr create_xe( + const std::shared_ptr& one_hot, + const std::shared_ptr& x) { + auto node_log = std::make_shared(x); - std::shared_ptr node_1_hot = label_2d; + auto node_mul = one_hot * node_log; + auto node_sum = std::make_shared( + node_mul, ngraph::AxisSet{x->get_shape().size() - 1}); + + auto shape = x->get_shape(); + shape.back() = 1; + return platform::NgReshaper(-node_sum, shape); +} +std::shared_ptr create_mask( + const std::shared_ptr& label, int ignore_index) { + auto ignore_node = paddle::platform::CreateConstant( + label->get_element_type(), label->get_shape(), {ignore_index}); + auto not_equal_node = + std::make_shared(label, ignore_node); + return not_equal_node; +} + +std::shared_ptr create_one_hot( + const std::shared_ptr& label, + const std::shared_ptr& x) { + auto label_shape = label->get_shape(); + return std::make_shared( + remove_trailing_one(label), x->get_shape(), x->get_shape().size() - 1); +} + +std::shared_ptr GetCrossEntropy( + std::shared_ptr x, std::shared_ptr label, + const bool is_soft_label, int ignore_index) { + std::shared_ptr node_1_hot = label; if (!is_soft_label) { - auto label_1d = - platform::NgReshaper(label_2d, ngraph::Shape{label_2d_shape.at(0)}); - node_1_hot = std::make_shared(label_1d, x_2d_shape, 1); - } - if (x->get_element_type() != node_1_hot->get_element_type()) { - node_1_hot = std::make_shared(node_1_hot, - x->get_element_type()); + node_1_hot = create_one_hot(label, x); } + node_1_hot = convert_to_node_type(node_1_hot, x); - auto node_log = std::make_shared(x_2d); - auto high_clip = ngraph::op::Constant::create(node_log->get_element_type(), - node_log->get_shape(), {1e20}); - auto low_clip = ngraph::op::Constant::create(node_log->get_element_type(), - node_log->get_shape(), {-1e20}); - auto node_min = std::make_shared(node_log, high_clip); - auto node_max = std::make_shared(node_min, low_clip); - auto node_mul = node_1_hot * node_log; - auto node_sum = - std::make_shared(node_mul, ngraph::AxisSet{1}); - auto node_neg = std::make_shared(node_sum); - auto xe = platform::NgReshaper(node_neg, ngraph::Shape{batch_size, 1}); - + auto xe = create_xe(node_1_hot, x); if (!is_soft_label) { - auto ignore_node = ngraph::op::Constant::create( - label->get_element_type(), label_2d_shape, {ignore_index}); - auto not_equal_node = - std::make_shared(label_2d, ignore_node); - auto mask = std::make_shared(not_equal_node, - xe->get_element_type()); + auto mask = convert_to_node_type(create_mask(label, ignore_index), xe); xe = xe * mask; } return xe; @@ -93,30 +116,17 @@ std::shared_ptr GetCrossEntropyGrad( std::shared_ptr mask; if (!is_soft_label) { - auto label_shape = label->get_shape(); - label_shape.pop_back(); - label = platform::NgReshaper(label, label_shape); - - auto ignore_node = ngraph::op::Constant::create( - label->get_element_type(), label_shape, {ignore_index}); - auto not_equal_node = - std::make_shared(label, ignore_node); - mask = std::make_shared(not_equal_node, - x->get_element_type()); - mask = std::make_shared(mask, x_shape, - ngraph::AxisSet{rank - 1}); - - label = std::make_shared(label, x_shape, rank - 1); + mask = convert_to_node_type(create_mask(label, ignore_index), x); + mask = std::make_shared( + remove_trailing_one(mask), x_shape, ngraph::AxisSet{rank - 1}); + label = create_one_hot(label, x); } - auto dy_shape = dy->get_shape(); - dy_shape.pop_back(); - auto dy_reshape = platform::NgReshaper(dy, dy_shape); + auto dy_reshape = remove_trailing_one(dy); auto dy_bcast = std::make_shared( dy_reshape, x_shape, ngraph::AxisSet{rank - 1}); - if (x->get_element_type() != label->get_element_type()) { - label = std::make_shared(label, x->get_element_type()); - } + + label = convert_to_node_type(label, x); auto xe_grad = -label * dy_bcast / x; @@ -154,9 +164,80 @@ void BuildCrossEntropyGradNode( auto xe_grad = GetCrossEntropyGrad(x, label, dy, is_soft_label, ignore_index); paddle::platform::SetOutputNode(op, "X@GRAD", xe_grad, ngb_node_map); } + +void BuildCrossEntropy2Node( + const std::shared_ptr& op, + std::shared_ptr< + std::unordered_map>> + ngb_node_map) { + auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map); + auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map); + auto op_attrs = paddle::framework::AttrReader(op->Attrs()); + int ignore_index = op_attrs.Get("ignore_index"); + + auto rank = x->get_shape().size(); + + auto one_hot = convert_to_node_type(create_one_hot(label, x), x); + auto xe = create_xe(one_hot, x); + auto mask = convert_to_node_type(create_mask(label, ignore_index), xe); + + xe = xe * mask; + + std::shared_ptr node_sum = + std::make_shared(one_hot * x, ngraph::AxisSet{rank - 1}); + node_sum = paddle::platform::NgReshaper(node_sum, mask->get_shape()); + auto matchx = mask * node_sum; + + paddle::platform::SetOutputNode(op, "MatchX", matchx, ngb_node_map); + platform::SetOutputNode(op, "XShape", x, ngb_node_map); + paddle::platform::SetOutputNode(op, "Y", xe, ngb_node_map); +} + +void BuildCrossEntropyGrad2Node( + const std::shared_ptr& op, + std::shared_ptr< + std::unordered_map>> + ngb_node_map) { + auto op_attrs = paddle::framework::AttrReader(op->Attrs()); + int ignore_index = op_attrs.Get("ignore_index"); + auto matchx = paddle::platform::GetInputNode(op, "MatchX", ngb_node_map); + auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map); + auto x = paddle::platform::GetInputNode(op, "XShape", ngb_node_map); + auto dy = paddle::platform::GetInputNode(op, framework::GradVarName("Y"), + ngb_node_map); + + matchx = remove_trailing_one(matchx); + label = remove_trailing_one(label); + x = remove_trailing_one(x); + dy = remove_trailing_one(dy); + + auto x_shape = x->get_shape(); + auto rank = x_shape.size(); + + auto one_hot = convert_to_node_type(create_one_hot(label, x), x); + auto mask = convert_to_node_type(create_mask(label, ignore_index), x); + + auto zero = paddle::platform::CreateConstant(matchx->get_element_type(), + matchx->get_shape(), {0}); + auto one = paddle::platform::CreateConstant(matchx->get_element_type(), + matchx->get_shape(), {1}); + auto is_zero = std::make_shared(matchx, zero); + matchx = std::make_shared(is_zero, one, matchx); + + auto dy_bcast = std::make_shared( + mask * dy, x_shape, ngraph::AxisSet{rank - 1}); + auto matchx_bcast = std::make_shared( + matchx, x_shape, ngraph::AxisSet{rank - 1}); + + auto xe_grad = -dy_bcast * one_hot / matchx_bcast; + paddle::platform::SetOutputNode(op, framework::GradVarName("X"), xe_grad, + ngb_node_map); +} } // namespace ngraphs } // namespace operators } // namespace paddle REGISTER_NG_OP(cross_entropy, BuildCrossEntropyNode); REGISTER_NG_OP(cross_entropy_grad, BuildCrossEntropyGradNode); +REGISTER_NG_OP(cross_entropy2, BuildCrossEntropy2Node); +REGISTER_NG_OP(cross_entropy_grad2, BuildCrossEntropyGrad2Node); diff --git a/paddle/fluid/operators/ngraph/ops/elementwise_binary_prepare_node.h b/paddle/fluid/operators/ngraph/ops/elementwise_binary_prepare_node.h index 8732932dedd4401853325b629877880cc90f6cb6..e4e17f5bb219bdf82db99fce2ea4fe5dbcb6e0c9 100644 --- a/paddle/fluid/operators/ngraph/ops/elementwise_binary_prepare_node.h +++ b/paddle/fluid/operators/ngraph/ops/elementwise_binary_prepare_node.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#include #include +#include #include #include "ngraph/ngraph.hpp" @@ -42,11 +44,11 @@ ngraph::NodeVector ElementwiseBinaryNodePrepare( if (lhs_shape == rhs_shape) { return ngraph::NodeVector{lhs, rhs}; } + axis = (rhs_shape.size() == 0) ? lhs_shape.size() - 1 : axis; axis = (axis == -1 ? lhs_shape.size() - rhs_shape.size() : axis); PADDLE_ENFORCE(axis >= 0 && axis < (int)(lhs_shape.size()), "Axis should be in range [0, lhs_shape)"); paddle::platform::TrimTrailingSingularDims(&rhs_shape); - axis = (rhs_shape.size() == 0) ? lhs_shape.size() : axis; int pre, n, post; paddle::platform::GetMidDims(lhs_shape, rhs_shape, axis, &pre, &n, &post); diff --git a/paddle/fluid/operators/ngraph/ops/elementwise_node.h b/paddle/fluid/operators/ngraph/ops/elementwise_node.h index 1e3f87aabe4c8595b781d9feafec9490fe514c12..2b10af4588c350e8581e304cdfdd075f56be53fd 100644 --- a/paddle/fluid/operators/ngraph/ops/elementwise_node.h +++ b/paddle/fluid/operators/ngraph/ops/elementwise_node.h @@ -1,4 +1,4 @@ -/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -14,10 +14,13 @@ limitations under the License. */ #pragma once +#include #include +#include #include "ngraph/ngraph.hpp" #include "paddle/fluid/operators/ngraph/ops/elementwise_binary_prepare_node.h" +#include "paddle/fluid/operators/ngraph/ops/op_bridge.h" #include "paddle/fluid/platform/ngraph_helper.h" namespace paddle { @@ -58,6 +61,17 @@ void BuildElementwiseCompareNode( auto out = std::make_shared(x, y); paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map); } + } // namespace ngraphs } // namespace operators } // namespace paddle + +REGISTER_NG_OP(elementwise_max, + BuildElementwiseBinaryNode); +REGISTER_NG_OP(elementwise_pow, BuildElementwiseBinaryNode); +REGISTER_NG_OP(elementwise_sub, + BuildElementwiseBinaryNode); +REGISTER_NG_OP(elementwise_min, + BuildElementwiseBinaryNode); +REGISTER_NG_OP(less_than, BuildElementwiseCompareNode); +REGISTER_NG_OP(elementwise_div, BuildElementwiseBinaryNode); diff --git a/paddle/fluid/operators/ngraph/ops/fill_constant_op.h b/paddle/fluid/operators/ngraph/ops/fill_constant_op.h index 42c2df5259242b7ae28613ab12c237834febc574..fee5f57e4862a8a033a28885a01a0dafea35f7f0 100644 --- a/paddle/fluid/operators/ngraph/ops/fill_constant_op.h +++ b/paddle/fluid/operators/ngraph/ops/fill_constant_op.h @@ -38,20 +38,9 @@ void BuildFillConstantNode( shape.push_back(sp); } float value = op_attrs.Get("value"); - ngraph::element::Type ng_dtype; - auto data_type = static_cast( - op_attrs.Get("dtype")); - if (data_type == paddle::framework::proto::VarType::FP32) { - ng_dtype = ngraph::element::f32; - } else if (data_type == paddle::framework::proto::VarType::FP64) { - ng_dtype = ngraph::element::f64; - } else if (data_type == paddle::framework::proto::VarType::INT64) { - ng_dtype = ngraph::element::i64; - } else if (data_type == paddle::framework::proto::VarType::INT32) { - ng_dtype = ngraph::element::i32; - } else { - PADDLE_THROW("unsupported data type: %s", data_type); - } + auto ng_dtype = + platform::GetNgType(static_cast( + op_attrs.Get("dtype"))); auto out = ngraph::op::Constant::create(ng_dtype, shape, {value}); paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map); } diff --git a/paddle/fluid/operators/ngraph/ops/mul_op.h b/paddle/fluid/operators/ngraph/ops/mul_op.h index d13665864b8950436298b7cf685c803593007803..cb46478ee8ad4f4c51a6ff9d6f5de4e66f6a505f 100644 --- a/paddle/fluid/operators/ngraph/ops/mul_op.h +++ b/paddle/fluid/operators/ngraph/ops/mul_op.h @@ -35,6 +35,7 @@ static void BuildMulNode( int y_num_col_dims = op_attrs.Get("y_num_col_dims"); auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map); auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map); + int y_rank = y->get_shape().size(); auto x_reshape = x; auto y_reshape = y; @@ -52,10 +53,14 @@ static void BuildMulNode( std::shared_ptr out = std::make_shared(x_reshape, y_reshape); - auto dummy_out = paddle::platform::GetOutputNode(op, "Out", ngb_node_map); - if (dummy_out && dummy_out->get_shape() != out->get_shape()) { - out = paddle::platform::NgReshaper(out, dummy_out->get_shape()); + ngraph::Shape out_shape; + for (int i = 0; i < x_num_col_dims; ++i) { + out_shape.push_back(x->get_shape()[i]); } + for (int i = y_num_col_dims; i < y_rank; ++i) { + out_shape.push_back(y->get_shape()[i]); + } + out = paddle::platform::NgReshaper(out, out_shape); paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map); } diff --git a/paddle/fluid/operators/ngraph/ops/pool2d_op.h b/paddle/fluid/operators/ngraph/ops/pool2d_op.h index c7b9c9316171a448d16ed68339f5754d25f3cabd..e5542d4715740ad9f2ab7315dcfa20434a08f3fa 100644 --- a/paddle/fluid/operators/ngraph/ops/pool2d_op.h +++ b/paddle/fluid/operators/ngraph/ops/pool2d_op.h @@ -60,17 +60,20 @@ void BuildPool2dNode( ngraph::Strides ng_strides{static_cast(strides.at(0)), static_cast(strides.at(1))}; - auto ComputeCeiledOutput = [](size_t in, size_t k, size_t p, size_t s) { + auto ComputeFlooredOutput = [](size_t in, size_t k, size_t p, size_t s) { return (in - k + 2 * p) / s + 1; }; + auto ComputeCeiledOutput = [](size_t in, size_t k, size_t p, size_t s) { + return ceil(static_cast(in - k + 2 * p) / s) + 1; + }; if (op_attrs.Get("ceil_mode")) { - auto dummy_out = paddle::platform::GetOutputNode(op, "Out", ngb_node_map); - auto dummpy_shape = dummy_out->get_shape(); for (size_t i = 0; i < ng_padding_above.size(); ++i) { - auto desired_size = ComputeCeiledOutput(x_shape[i + 2], ksize[i], - paddings[i], strides[i]); - if (desired_size != dummpy_shape[i + 2]) { + auto ceiled_size = ComputeCeiledOutput(x_shape[i + 2], ksize[i], + paddings[i], strides[i]); + auto floored_size = ComputeFlooredOutput(x_shape[i + 2], ksize[i], + paddings[i], strides[i]); + if (ceiled_size != floored_size) { ng_padding_above[i] += strides[i]; } } @@ -96,6 +99,10 @@ void BuildPool2dNode( pool2d = std::make_shared(x, ng_ksize_shape, ng_strides); } else { + if ((ng_padding_below[0] == 0) && (ng_padding_below[1] == 0) && + (ng_padding_above[0] == 0) && (ng_padding_above[1] == 0)) { + padding_exclusive = false; + } pool2d = std::make_shared( x, ng_ksize_shape, ng_strides, ng_padding_below, ng_padding_above, !padding_exclusive); @@ -163,6 +170,10 @@ void BuildPool2dGradNode( x->get_shape(), dout, ng_ksize_shape, ng_strides, ng_padding_below, ng_padding_above, !padding_exclusive); } else { + if ((ng_padding_below[0] == 0) && (ng_padding_below[1] == 0) && + (ng_padding_above[0] == 0) && (ng_padding_above[1] == 0)) { + padding_exclusive = false; + } pool2d_grad = std::make_shared( x->get_shape(), dout, ng_ksize_shape, ng_strides, ng_padding_below, ng_padding_above, !padding_exclusive); diff --git a/paddle/fluid/operators/one_hot_op.cc b/paddle/fluid/operators/one_hot_op.cc index 626895f49d8d4347f1e9a40526943cf00c73d034..cbb0c4028b3daa927529456e76253d93857a58b5 100644 --- a/paddle/fluid/operators/one_hot_op.cc +++ b/paddle/fluid/operators/one_hot_op.cc @@ -13,6 +13,8 @@ // limitations under the License. #include "paddle/fluid/operators/one_hot_op.h" +#include +#include #include "paddle/fluid/framework/framework.pb.h" namespace paddle { @@ -34,15 +36,34 @@ class OneHotOp : public framework::OperatorWithKernel { PADDLE_ENFORCE_GE(x_dims[x_dims.size() - 1], 1U, "Last dimension of Input(X) should be 1."); } - int depth = ctx->Attrs().Get("depth"); - - PADDLE_ENFORCE_GT(depth, 0, "Should provide a positive depth (%d).", depth); framework::DDim out_dims(x_dims); + int depth = ctx->Attrs().Get("depth"); + if (ctx->HasInput("depth_tensor")) { + depth = -1; + } + out_dims[out_dims.size() - 1] = depth; ctx->SetOutputDim("Out", out_dims); ctx->ShareLoD("X", /* --> */ "Out"); } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + return framework::OpKernelType(ctx.Input("X")->type(), + ctx.device_context()); + } + + framework::OpKernelType GetKernelTypeForVar( + const std::string& var_name, const Tensor& tensor, + const framework::OpKernelType& expected_kernel_type) const override { + if (var_name == "depth_tensor") { + return expected_kernel_type; + } + return framework::OpKernelType(expected_kernel_type.data_type_, + tensor.place(), tensor.layout()); + } }; class OneHotOpMaker : public framework::OpProtoAndCheckerMaker { @@ -52,11 +73,15 @@ class OneHotOpMaker : public framework::OpProtoAndCheckerMaker { "(LoDTensor, LoDTensor) Input variable with rank at least 2. " "The last dimension of X should be 1. Each value of X is an index " "to indicate the position."); + AddInput("depth_tensor", "(Tensor, Tensor), Length of one-hot vector") + .AsDispensable(); AddOutput("Out", "(Tensor, Tensor) Output tensor with same rank as X. " "The tensor consists of one-hot representations of values in X."); + AddAttr("depth", - "A positive integer to specify the length of one-hot vector."); + "A positive integer to specify the length of one-hot vector.") + .SetDefault(-1); AddAttr("dtype", "An integer to specify the data type of one-hot " "vector. The default value is FP32.") diff --git a/paddle/fluid/operators/one_hot_op.cu b/paddle/fluid/operators/one_hot_op.cu index 59d8b9b8a8d554eb16826712ff634eed5df2d648..b9fe0bf2e9dc46ecc3974455e1328d8a83bcf388 100644 --- a/paddle/fluid/operators/one_hot_op.cu +++ b/paddle/fluid/operators/one_hot_op.cu @@ -62,8 +62,25 @@ class OneHotCUDAKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext& context) const override { auto* in = context.Input("X"); auto* out = context.Output("Out"); - int depth = context.Attr("depth"); + int depth = -1; + if (context.HasInput("depth_tensor")) { + auto* depth_tensor = context.Input("depth_tensor"); + if (platform::is_gpu_place(depth_tensor->place())) { + framework::Tensor temp; + TensorCopySync(*depth_tensor, platform::CPUPlace(), &temp); + depth = *temp.data(); + } else { + depth = *depth_tensor->data(); + } + + auto in_dims = in->dims(); + framework::DDim out_dims(in_dims); + out_dims[out_dims.size() - 1] = depth; + out->Resize(out_dims); + } else { + depth = context.Attr("depth"); + } framework::VisitDataType( static_cast( context.Attr("dtype")), diff --git a/paddle/fluid/operators/one_hot_op.h b/paddle/fluid/operators/one_hot_op.h index 1ebd2676496940ff8f90caaaded5c8227bd7ae78..7273080927ecd9b35d72c272e2d8b4254a0c3991 100644 --- a/paddle/fluid/operators/one_hot_op.h +++ b/paddle/fluid/operators/one_hot_op.h @@ -49,6 +49,7 @@ struct OneHotOpFunctor { }; using LoDTensor = framework::LoDTensor; +using Tensor = framework::Tensor; template class OneHotKernel : public framework::OpKernel { public: @@ -56,6 +57,15 @@ class OneHotKernel : public framework::OpKernel { auto* in = context.Input("X"); auto* out = context.Output("Out"); int depth = context.Attr("depth"); + if (context.HasInput("depth_tensor")) { + auto* depth_tensor = context.Input("depth_tensor"); + auto* depth_data = depth_tensor->data(); + depth = depth_data[0]; + auto in_dims = in->dims(); + framework::DDim out_dims(in_dims); + out_dims[out_dims.size() - 1] = depth; + out->Resize(out_dims); + } framework::VisitDataType( static_cast( diff --git a/paddle/fluid/operators/optimizers/adam_op.cc b/paddle/fluid/operators/optimizers/adam_op.cc index 54e0f5146dab3e19713d19e15c6c81868179b319..dd347aa0afebe5c75e7f3b574083783b4454fd20 100644 --- a/paddle/fluid/operators/optimizers/adam_op.cc +++ b/paddle/fluid/operators/optimizers/adam_op.cc @@ -18,67 +18,64 @@ namespace paddle { namespace operators { using Tensor = framework::Tensor; -class AdamOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - void InferShape(framework::InferShapeContext *ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("Param"), - "Input(Param) of AdamOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Grad"), - "Input(Grad) of AdamOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Moment1"), - "Input(Moment1) of AdamOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Moment2"), - "Input(Moment2) of AdamOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("LearningRate"), - "Input(LearningRate) of AdamOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Beta1Pow"), - "Input(Beta1Pow) of AdamOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Beta2Pow"), - "Input(Beta2Pow) of AdamOp should not be null."); - - PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), - "Output(ParamOut) of AdamOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Moment1Out"), - "Output(Moment1Out) of AdamOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Moment2Out"), - "Output(Moment2Out) of AdamOp should not be null."); - - auto lr_dims = ctx->GetInputDim("LearningRate"); - PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1, - "Learning rate should have 1 dimension"); - auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow"); - PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1, - "Beta1 power accumulator should have 1 dimension"); - auto beta2_pow_dims = ctx->GetInputDim("Beta2Pow"); - PADDLE_ENFORCE_EQ(framework::product(beta2_pow_dims), 1, - "Beta2 power accumulator should have 1 dimension"); - - auto param_dims = ctx->GetInputDim("Param"); - if (ctx->GetInputsVarType("Grad")[0] == - framework::proto::VarType::LOD_TENSOR) { - PADDLE_ENFORCE_EQ( - param_dims, ctx->GetInputDim("Grad"), - "Param and Grad input of AdamOp should have same dimension"); - } - PADDLE_ENFORCE_EQ( - param_dims, ctx->GetInputDim("Moment1"), - "Param and Moment1 input of AdamOp should have same dimension"); - PADDLE_ENFORCE_EQ( - param_dims, ctx->GetInputDim("Moment2"), - "Param and Moment2 input of AdamOp should have same dimension"); - ctx->SetOutputDim("ParamOut", param_dims); - ctx->SetOutputDim("Moment1Out", param_dims); - ctx->SetOutputDim("Moment2Out", param_dims); - } - framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext &ctx) const override { - auto input_data_type = ctx.Input("Param")->type(); - return framework::OpKernelType(input_data_type, ctx.GetPlace()); +void AdamOp::InferShape(framework::InferShapeContext* ctx) const { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Moment1"), + "Input(Moment1) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Moment2"), + "Input(Moment2) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("LearningRate"), + "Input(LearningRate) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Beta1Pow"), + "Input(Beta1Pow) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Beta2Pow"), + "Input(Beta2Pow) of AdamOp should not be null."); + + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(ParamOut) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Moment1Out"), + "Output(Moment1Out) of AdamOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Moment2Out"), + "Output(Moment2Out) of AdamOp should not be null."); + + auto lr_dims = ctx->GetInputDim("LearningRate"); + PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1, + "Learning rate should have 1 dimension"); + auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow"); + PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1, + "Beta1 power accumulator should have 1 dimension"); + auto beta2_pow_dims = ctx->GetInputDim("Beta2Pow"); + PADDLE_ENFORCE_EQ(framework::product(beta2_pow_dims), 1, + "Beta2 power accumulator should have 1 dimension"); + + auto param_dims = ctx->GetInputDim("Param"); + if (ctx->GetInputsVarType("Grad")[0] == + framework::proto::VarType::LOD_TENSOR) { + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Grad"), + "Param and Grad input of AdamOp should have same dimension"); } -}; + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Moment1"), + "Param and Moment1 input of AdamOp should have same dimension"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Moment2"), + "Param and Moment2 input of AdamOp should have same dimension"); + + ctx->SetOutputDim("ParamOut", param_dims); + ctx->SetOutputDim("Moment1Out", param_dims); + ctx->SetOutputDim("Moment2Out", param_dims); +} + +framework::OpKernelType AdamOp::GetExpectedKernelType( + const framework::ExecutionContext& ctx) const { + auto input_data_type = ctx.Input("Param")->type(); + return framework::OpKernelType(input_data_type, ctx.GetPlace()); +} class AdamOpMaker : public framework::OpProtoAndCheckerMaker { public: diff --git a/paddle/fluid/operators/optimizers/adam_op.h b/paddle/fluid/operators/optimizers/adam_op.h index 6262ef0c2d3802bca574ba1312e7cf4a720403ef..1cc34f11d09e9ec1868249f20fcc1b189efb0589 100644 --- a/paddle/fluid/operators/optimizers/adam_op.h +++ b/paddle/fluid/operators/optimizers/adam_op.h @@ -29,6 +29,15 @@ namespace operators { namespace scatter = paddle::operators::math::scatter; +class AdamOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext* ctx) const override; + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override; +}; + struct GPUAdam; struct CPUAdam; diff --git a/paddle/fluid/operators/print_op.cc b/paddle/fluid/operators/print_op.cc index 6a5bf170600661fb8ed64ae070da4d3c03c217bd..f686e5293b0f504863e228d37db56c6df4954c24 100644 --- a/paddle/fluid/operators/print_op.cc +++ b/paddle/fluid/operators/print_op.cc @@ -26,6 +26,19 @@ const char kForward[] = "FORWARD"; const char kBackward[] = "BACKWARD"; const char kBoth[] = "BOTH"; +class LogGuard { + public: + inline LogGuard() { LogMutex().lock(); } + + inline ~LogGuard() { LogMutex().unlock(); } + + private: + static std::mutex &LogMutex() { + static std::mutex mtx; + return mtx; + } +}; + struct Formater { std::string message; std::string name; @@ -34,48 +47,54 @@ struct Formater { framework::LoD lod; int summarize; void *data{nullptr}; + platform::Place place; + std::stringstream logs; void operator()(size_t size) { PrintMessage(); + PrintPlaceInfo(); PrintName(); PrintDims(); PrintDtype(); PrintLod(); PrintData(size); + LogGuard guard; + CLOG << logs.str(); } private: - void PrintMessage() { CLOG << std::time(nullptr) << "\t" << message << "\t"; } + void PrintPlaceInfo() { logs << "The place is:" << place << std::endl; } + void PrintMessage() { logs << std::time(nullptr) << "\t" << message << "\t"; } void PrintName() { if (!name.empty()) { - CLOG << "Tensor[" << name << "]" << std::endl; + logs << "Tensor[" << name << "]" << std::endl; } } void PrintDims() { if (!dims.empty()) { - CLOG << "\tshape: ["; + logs << "\tshape: ["; for (auto i : dims) { - CLOG << i << ","; + logs << i << ","; } - CLOG << "]" << std::endl; + logs << "]" << std::endl; } } void PrintDtype() { if (!framework::IsType(dtype)) { - CLOG << "\tdtype: " << dtype.name() << std::endl; + logs << "\tdtype: " << dtype.name() << std::endl; } } void PrintLod() { if (!lod.empty()) { - CLOG << "\tLoD: ["; + logs << "\tLoD: ["; for (auto level : lod) { - CLOG << "[ "; + logs << "[ "; for (auto i : level) { - CLOG << i << ","; + logs << i << ","; } - CLOG << " ]"; + logs << " ]"; } - CLOG << "]" << std::endl; + logs << "]" << std::endl; } } @@ -93,56 +112,57 @@ struct Formater { } else if (framework::IsType(dtype)) { Display(size); } else { - CLOG << "\tdata: unprintable type: " << dtype.name() << std::endl; + logs << "\tdata: unprintable type: " << dtype.name() << std::endl; } } template void Display(size_t size) { auto *d = reinterpret_cast(data); - CLOG << "\tdata: "; + logs << "\tdata: "; if (summarize != -1) { summarize = std::min(size, (size_t)summarize); for (int i = 0; i < summarize; i++) { - CLOG << d[i] << ","; + logs << d[i] << ","; } } else { for (size_t i = 0; i < size; i++) { - CLOG << d[i] << ","; + logs << d[i] << ","; } } - CLOG << std::endl; + logs << std::endl; } }; // TODO(ChunweiYan) there should be some other printers for TensorArray -class TensorPrintOp : public framework::OperatorBase { +class PrintOp : public framework::OperatorBase { public: - TensorPrintOp(const std::string &type, - const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) + PrintOp(const std::string &type, const framework::VariableNameMap &inputs, + const framework::VariableNameMap &outputs, + const framework::AttributeMap &attrs) : OperatorBase(type, inputs, outputs, attrs) {} - TensorPrintOp(const TensorPrintOp &o) - : framework::OperatorBase( - static_cast(o)) { - PADDLE_THROW("Not implemented."); - } - private: void RunImpl(const framework::Scope &scope, const platform::Place &place) const override { - const framework::Variable *in_var_ptr = nullptr; - std::string printed_var_name = ""; - - in_var_ptr = scope.FindVar(Input("In")); - printed_var_name = Inputs("In").front(); - - PADDLE_ENFORCE_NOT_NULL(in_var_ptr); - - auto &in_tensor = in_var_ptr->Get(); + const auto in_var = scope.FindVar(Input("In")); + auto out_var = scope.FindVar(Output("Out")); + PADDLE_ENFORCE_NOT_NULL(in_var, "The input should not be found in scope", + Input("In")); + PADDLE_ENFORCE_NOT_NULL(out_var, "The output should not be found in scope", + Output("Out")); + auto &in_tensor = in_var->Get(); + framework::LoDTensor *out_tensor = + out_var->GetMutable(); + + PrintValue(place, Inputs("In").front(), in_tensor); + framework::TensorCopy(in_tensor, place, out_tensor); + out_tensor->set_lod(in_tensor.lod()); + } + void PrintValue(const platform::Place &place, + const std::string &printed_var_name, + const framework::LoDTensor &in_tensor) const { std::string print_phase = Attr("print_phase"); bool is_forward = Attr("is_forward"); @@ -158,15 +178,16 @@ class TensorPrintOp : public framework::OperatorBase { printed_tensor.set_lod(in_tensor.lod()); printed_tensor.Resize(in_tensor.dims()); - if (platform::is_cpu_place(in_tensor.place())) { + if (is_cpu_place(in_tensor.place())) { printed_tensor.ShareDataWith(in_tensor); } else { // copy data to cpu to print platform::CPUPlace place; - framework::TensorCopy(in_tensor, place, &printed_tensor); + TensorCopy(in_tensor, place, &printed_tensor); } Formater formater; + formater.place = place; formater.message = Attr("message"); if (Attr("print_tensor_name")) { formater.name = printed_var_name; @@ -195,6 +216,7 @@ class PrintOpProtoAndCheckMaker : public framework::OpProtoAndCheckerMaker { public: void Make() override { AddInput("In", "Input tensor to be displayed."); + AddOutput("Out", "The output tensor."); AddAttr("first_n", "Only log `first_n` number of times."); AddAttr("message", "A string message to print as a prefix."); AddAttr("summarize", "Number of elements printed."); @@ -219,10 +241,23 @@ tensor `t`.)DOC"); } }; -class InferShapeForward : public framework::InferShapeBase { +class PrintOpInferShape : public framework::InferShapeBase { + public: + void operator()(framework::InferShapeContext *ctx) const override { + VLOG(10) << "PrintOpInferShape"; + PADDLE_ENFORCE(ctx->HasInput("In"), "Input(In) should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null."); + ctx->ShareDim("In", /*->*/ "Out"); + ctx->ShareLoD("In", /*->*/ "Out"); + } +}; + +class PrintOpVarTypeInference : public framework::VarTypeInference { public: - void operator()(framework::InferShapeContext *context) const override { - PADDLE_ENFORCE(context->HasInput("In"), "Input(In) should not be null."); + void operator()(framework::InferVarTypeContext *ctx) const override { + auto input_type = ctx->GetType(ctx->Input("In")[0]); + auto out_name = ctx->Output("Out").front(); + ctx->SetType(out_name, input_type); } }; @@ -233,7 +268,8 @@ class PrintOpGradientMaker : public framework::SingleGradOpDescMaker { std::unique_ptr Apply() const override { auto *op_desc_ptr = new framework::OpDesc(); op_desc_ptr->SetType("print"); - op_desc_ptr->SetInput("In", InputGrad("In")); + op_desc_ptr->SetInput("In", OutputGrad("Out")); + op_desc_ptr->SetOutput("Out", InputGrad("In")); op_desc_ptr->SetAttrMap(Attrs()); op_desc_ptr->SetAttr("is_forward", false); return std::unique_ptr(op_desc_ptr); @@ -245,5 +281,6 @@ class PrintOpGradientMaker : public framework::SingleGradOpDescMaker { namespace ops = paddle::operators; -REGISTER_OPERATOR(print, ops::TensorPrintOp, ops::PrintOpProtoAndCheckMaker, - ops::PrintOpGradientMaker, ops::InferShapeForward); +REGISTER_OPERATOR(print, ops::PrintOp, ops::PrintOpProtoAndCheckMaker, + ops::PrintOpGradientMaker, ops::PrintOpInferShape, + ops::PrintOpVarTypeInference); diff --git a/paddle/fluid/operators/reader/buffered_reader.cc b/paddle/fluid/operators/reader/buffered_reader.cc index 418c342c8fc403c09891031d958b0aa91ad3b476..16cb08f4190a3b76f4795b838697dca81e67e007 100644 --- a/paddle/fluid/operators/reader/buffered_reader.cc +++ b/paddle/fluid/operators/reader/buffered_reader.cc @@ -14,6 +14,7 @@ #include "paddle/fluid/operators/reader/buffered_reader.h" #include +#include #include #include "paddle/fluid/framework/data_type.h" @@ -167,7 +168,8 @@ void BufferedReader::ReadNextImpl(std::vector *out) { return; } - *out = platform::is_gpu_place(place_) ? gpu_buffer_[i] : cpu_buffer_[i]; + *out = std::move(platform::is_gpu_place(place_) ? gpu_buffer_[i] + : cpu_buffer_[i]); // Do not push current position into ReadAsync. Push the previous position // Since all computation in fluid are async, change the data of diff --git a/paddle/fluid/operators/reader/ctr_reader.cc b/paddle/fluid/operators/reader/ctr_reader.cc index 43a49de52242b96aade91013e89228fcb3247302..4edc15a2635ef84a889a7e9267de311a7297fea6 100644 --- a/paddle/fluid/operators/reader/ctr_reader.cc +++ b/paddle/fluid/operators/reader/ctr_reader.cc @@ -32,17 +32,17 @@ namespace reader { static inline void string_split(const std::string& s, const char delimiter, std::vector* output) { - size_t start = 0; - size_t end = s.find_first_of(delimiter); + if (s.empty()) return; - while (end <= std::string::npos) { - output->emplace_back(s.substr(start, end - start)); - if (end == std::string::npos) { - break; - } + size_t start = 0; + size_t end = s.find(delimiter); + while (end != std::string::npos) { + if (end > start) output->emplace_back(s.substr(start, end - start)); start = end + 1; - end = s.find_first_of(delimiter, start); + end = s.find(delimiter, start); } + auto term = s.substr(start); + if (!term.empty()) output->emplace_back(term); } static inline void parse_line( @@ -52,9 +52,9 @@ static inline void parse_line( std::unordered_map>* slot_to_data) { std::vector ret; string_split(line, ' ', &ret); - *label = std::stoi(ret[2]) > 0; + *label = std::stoi(ret[0]) > 0; - for (size_t i = 3; i < ret.size(); ++i) { + for (size_t i = 1; i < ret.size(); ++i) { const std::string& item = ret[i]; std::vector feasign_and_slot; string_split(item, ':', &feasign_and_slot); diff --git a/paddle/fluid/operators/reader/ctr_reader_test.cc b/paddle/fluid/operators/reader/ctr_reader_test.cc index 6410439816d8ae4a9d1df507819071ce76b5308e..b64c8400439885e0327f612994d1736f3385b5bd 100644 --- a/paddle/fluid/operators/reader/ctr_reader_test.cc +++ b/paddle/fluid/operators/reader/ctr_reader_test.cc @@ -91,16 +91,16 @@ static inline void check_all_data( TEST(CTR_READER, read_data) { const std::vector ctr_data = { - "aaaa 1 0 0:6002 1:6003 2:6004 3:6005 4:6006 -1\n", - "bbbb 1 0 5:6003 6:6003 7:6003 8:6004 9:6004 -1\n", - "cccc 1 1 10:6002 11:6002 12:6002 13:6002 14:6002 -2\n", - "dddd 1 0 15:6003 16:6003 17:6003 18:6003 19:6004 -3\n", - "1111 1 1 20:6001 21:6001 22:6001 23:6001 24:6001 12\n", - "2222 1 1 25:6004 26:6004 27:6004 28:6005 29:6005 aa\n", - "3333 1 0 30:6002 31:6003 32:6004 33:6004 34:6005 er\n", - "eeee 1 1 35:6003 36:6003 37:6005 38:6005 39:6005 dd\n", - "ffff 1 1 40:6002 41:6003 42:6004 43:6004 44:6005 66\n", - "gggg 1 1 46:6006 45:6006 47:6003 48:6003 49:6003 ba\n", + "0 0:6002 1:6003 2:6004 3:6005 4:6006 \n", + "0 5:6003 6:6003 7:6003 8:6004 9:6004 \n", + "1 10:6002 11:6002 12:6002 13:6002 14:6002 \n", + "0 15:6003 16:6003 17:6003 18:6003 19:6004 \n", + "1 20:6001 21:6001 22:6001 23:6001 24:6001 \n", + "1 25:6004 26:6004 27:6004 28:6005 29:6005 \n", + "0 30:6002 31:6003 32:6004 33:6004 34:6005 \n", + "1 35:6003 36:6003 37:6005 38:6005 39:6005 \n", + "1 40:6002 41:6003 42:6004 43:6004 44:6005 \n", + "1 46:6006 45:6006 47:6003 48:6003 49:6003 \n", }; std::string gz_file_name = "test_ctr_reader_data.gz"; generatedata(ctr_data, gz_file_name); diff --git a/paddle/fluid/operators/recurrent_op.cc b/paddle/fluid/operators/recurrent_op.cc index 1a2feee11c951cd4a55958df58f3756472f64769..b3bb1abf4da6fcd8f591971d56232490b8519300 100644 --- a/paddle/fluid/operators/recurrent_op.cc +++ b/paddle/fluid/operators/recurrent_op.cc @@ -37,6 +37,20 @@ constexpr char kInitStateGrads[] = "initial_states" GRAD_SUFFIX; using StepScopeVar = std::vector; +static void ClearStepScopes(const platform::DeviceContext &dev_ctx, + framework::Scope *parent_scope, + StepScopeVar *step_scopes) { + if (step_scopes->empty()) return; + + dev_ctx.Wait(); + + for (auto *sub_scope : *step_scopes) { + parent_scope->DeleteScope(sub_scope); + } + + step_scopes->clear(); +} + // StepScopes manages scopes inside RNN. // StepScopes::CurScope() get the current scope // StepScopes::ExScope() get the ex-scope, or scope in previous time step. @@ -53,7 +67,8 @@ using StepScopeVar = std::vector; // access scopes from begin to end. class StepScopes { public: - StepScopes(const framework::Scope &parent, StepScopeVar *scopes, + StepScopes(const platform::DeviceContext &dev_ctx, + const framework::Scope &parent, StepScopeVar *scopes, bool is_train, size_t seq_len, bool is_backward = false) : counter_(is_backward ? seq_len - 1 : 0UL), scopes_(scopes), @@ -63,7 +78,7 @@ class StepScopes { PADDLE_ENFORCE(is_train || !is_backward, "Cannot backward when is not training"); if (!is_backward_) { - PADDLE_ENFORCE(scopes->empty()); + ClearStepScopes(dev_ctx, const_cast(&parent), scopes); scopes->reserve(static_cast(num_step_scopes)); for (size_t i = 0; i < num_step_scopes; ++i) { scopes->emplace_back(&parent.NewScope()); @@ -244,18 +259,22 @@ class RecurrentOp : public RecurrentBase { const platform::Place &place) const override { bool has_state = Attr(kHasStates); auto seq_len = static_cast(this->GetSequenceLength(scope)); - VLOG(3) << "Static RNN input sequence length = " << seq_len; - StepScopes scopes = CreateStepScopes(scope, seq_len); - auto reverse = Attr(kReverse); // get device context from pool platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance(); auto &dev_ctx = *pool.Get(place); + VLOG(3) << "Static RNN input sequence length = " << seq_len; + StepScopes scopes = CreateStepScopes(dev_ctx, scope, seq_len); + auto reverse = Attr(kReverse); + framework::Executor executor(place); auto *block = Attr(kStepBlock); auto *program = block->Program(); + auto ctx = executor.Prepare( + *program, block->ID(), std::vector() /*skip_ref_cnt_vars*/, + true /*force_disable_gc*/); for (size_t i = 0; i < seq_len; ++i) { size_t seq_offset = reverse ? seq_len - i - 1 : i; @@ -289,10 +308,9 @@ class RecurrentOp : public RecurrentBase { } // Every inputs are linked now, execute! - executor.Run(*program, &cur_scope, block->ID(), - false /*create_local_scope*/, true /*create_vars*/, - std::vector() /*skip_ref_cnt_vars*/, - true /*force_disable_gc*/); + executor.RunPreparedContext(ctx.get(), &cur_scope, + false /*create_local_scope*/, + true /*create_vars*/, true /* keep_kids */); // Copy inside::output -> outside::output // outside::output[seq_offset: seq_offset + 1] = inside::output @@ -316,11 +334,12 @@ class RecurrentOp : public RecurrentBase { } private: - StepScopes CreateStepScopes(const framework::Scope &scope, + StepScopes CreateStepScopes(const platform::DeviceContext &dev_ctx, + const framework::Scope &scope, size_t seq_len) const { auto *var = scope.FindVar(Output(kStepScopes)); PADDLE_ENFORCE(var != nullptr); - return StepScopes(scope, var->GetMutable(), + return StepScopes(dev_ctx, scope, var->GetMutable(), Attr(kIsTrain), seq_len); } }; @@ -338,16 +357,20 @@ class RecurrentGradOp : public RecurrentBase { const platform::Place &place) const override { bool has_state = Attr(kHasStates); const size_t seq_len = static_cast(GetSequenceLength(scope)); - StepScopes scopes = CreateStepScopes(scope, seq_len); + + // get device context from pool + platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance(); + auto &dev_ctx = *pool.Get(place); + + StepScopes scopes = CreateStepScopes(dev_ctx, scope, seq_len); auto reverse = Attr(kReverse); framework::Executor executor(place); auto *block = Attr(kStepBlock); auto *program = block->Program(); - - // get device context from pool - platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance(); - auto &dev_ctx = *pool.Get(place); + auto ctx = executor.Prepare( + *program, block->ID(), std::vector() /*skip_ref_cnt_vars*/, + true /*force_disable_gc*/); for (size_t step_id = 0; step_id < seq_len; ++step_id) { size_t seq_offset = reverse ? step_id : seq_len - step_id - 1; @@ -405,10 +428,9 @@ class RecurrentGradOp : public RecurrentBase { VLOG(5) << "Recurrent memory linking finished "; // Run step block with cur_scope - executor.Run(*program, &cur_scope, block->ID(), - false /*create_local_scope*/, true /*create_vars*/, - std::vector() /*skip_ref_cnt_vars*/, - true /*force_disable_gc*/); + executor.RunPreparedContext(ctx.get(), &cur_scope, + false /*create_local_scope*/, + true /*create_vars*/, true /* keep_kids */); VLOG(5) << "executor.Run finished "; @@ -501,21 +523,20 @@ class RecurrentGradOp : public RecurrentBase { scopes.Next(); } // Delete the scope of StepScopes - dev_ctx.Wait(); auto *var = scope.FindVar(Input(kStepScopes)); PADDLE_ENFORCE(var != nullptr); - auto step_scopes = var->GetMutable(); - for (auto *sub_scope : *step_scopes) { - const_cast(scope).DeleteScope(sub_scope); - } + auto *step_scopes = var->GetMutable(); + ClearStepScopes(dev_ctx, const_cast(&scope), + step_scopes); } private: - StepScopes CreateStepScopes(const framework::Scope &scope, + StepScopes CreateStepScopes(const platform::DeviceContext &dev_ctx, + const framework::Scope &scope, size_t seq_len) const { auto *var = scope.FindVar(Input(kStepScopes)); PADDLE_ENFORCE(var != nullptr); - return StepScopes(scope, var->GetMutable(), + return StepScopes(dev_ctx, scope, var->GetMutable(), Attr(kIsTrain), seq_len, true /*is_backward*/); } diff --git a/paddle/fluid/operators/reduce_ops/reduce_mean_op.cc b/paddle/fluid/operators/reduce_ops/reduce_mean_op.cc index 072bc34d3e23a48c8d856a51b0d5a6facc7ececf..d1b508792c255fc650459ebf308665551e1f8bde 100644 --- a/paddle/fluid/operators/reduce_ops/reduce_mean_op.cc +++ b/paddle/fluid/operators/reduce_ops/reduce_mean_op.cc @@ -13,8 +13,67 @@ // limitations under the License. #include "paddle/fluid/operators/reduce_ops/reduce_mean_op.h" +#include +#include +#include -REGISTER_REDUCE_OP(reduce_mean); +namespace paddle { +namespace operators { + +// NOTE(dengkaipeng): Input(Out) is unnecessary in reduce_mean_grad +// calcualtion, but will incur a reduce_mean_grad op after +// reduce_mean_grad_grad, delete Input(Out) here. +// This change has no effect on reduce_mean_grad calculations. +class ReduceMeanOpGradDescMaker : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + std::unique_ptr op(new framework::OpDesc()); + op->SetType("reduce_mean_grad"); + op->SetInput("X", Input("X")); + op->SetInput(framework::GradVarName("Out"), OutputGrad("Out")); + op->SetAttrMap(Attrs()); + op->SetOutput(framework::GradVarName("X"), InputGrad("X")); + return op; + } +}; + +class ReduceMeanDoubleGradMaker : public framework::GradOpDescMakerBase { + public: + using framework::GradOpDescMakerBase::GradOpDescMakerBase; + + std::vector> operator()() const override { + std::vector> ops; + auto x_gg = OutputGrad(framework::GradVarName("X")); // input ddx + auto out_grads = InputGrad(framework::GradVarName("Out")); + if (!out_grads.empty()) { + auto* out_grad_op = new framework::OpDesc(); + out_grad_op->SetType("reduce_mean"); + out_grad_op->SetInput("X", x_gg); + out_grad_op->SetAttrMap(Attrs()); + out_grad_op->SetOutput("Out", out_grads); + ops.emplace_back(out_grad_op); + } + + return ops; + } +}; + +} // namespace operators +} // namespace paddle + +class __reduce_meanMaker__ : public ops::ReduceOpMaker { + protected: + virtual std::string GetName() const { return "reduce_mean"; } + virtual std::string GetOpType() const { return "Reduce reduce_mean"; } +}; + +REGISTER_OPERATOR(reduce_mean, ops::ReduceOp, __reduce_meanMaker__, + ops::ReduceMeanOpGradDescMaker); +REGISTER_OPERATOR(reduce_mean_grad, ops::ReduceGradOp, + ops::ReduceMeanDoubleGradMaker); REGISTER_OP_CPU_KERNEL(reduce_mean, ops::ReduceKernel, diff --git a/paddle/fluid/operators/reduce_ops/reduce_op.h b/paddle/fluid/operators/reduce_ops/reduce_op.h index c86591fdafa3d33bb3c7d75bf9f4f3b041a7a9cb..67fd3e1dad4b9c6036ac2c8f7f0fe5ec951c8e98 100644 --- a/paddle/fluid/operators/reduce_ops/reduce_op.h +++ b/paddle/fluid/operators/reduce_ops/reduce_op.h @@ -88,6 +88,10 @@ class ReduceGradKernel : public framework::OpKernel { auto* output = context.Output(framework::GradVarName("X")); output->mutable_data(context.GetPlace()); + // NOTE(dengkaipeng): Out is unnecessary in some reduce kernel and + // not be set as Input in grad Maker, use Out_grad to replace here + if (!input1) input1 = input2; + if (reduce_all) { auto x = EigenVector::Flatten(*input0); auto x_reduce = EigenVector::From(*input1); diff --git a/paddle/fluid/operators/reshape_op.cc b/paddle/fluid/operators/reshape_op.cc index f3719e8f438f6365414a1e91192a863fd451209d..9750bc87b001e034cb65463101ba57fbbc105eca 100644 --- a/paddle/fluid/operators/reshape_op.cc +++ b/paddle/fluid/operators/reshape_op.cc @@ -19,6 +19,29 @@ limitations under the License. */ namespace paddle { namespace operators { +using Tensor = framework::Tensor; + +inline std::vector get_new_shape( + const std::vector &list_new_shape_tensor) { + // get tensor from + std::vector vec_new_shape; + for (size_t i = 0; i < list_new_shape_tensor.size(); ++i) { + auto tensor = list_new_shape_tensor[i]; + PADDLE_ENFORCE_EQ(tensor->dims(), framework::make_ddim({1}), + "shape of dim tensor should be [1]"); + if (platform::is_gpu_place(tensor->place())) { + framework::Tensor temp; + TensorCopySync(*tensor, platform::CPUPlace(), &temp); + + vec_new_shape.push_back(static_cast(*temp.data())); + } else { + vec_new_shape.push_back(static_cast(*tensor->data())); + } + } + + return vec_new_shape; +} + class ReshapeOp : public framework::OperatorWithKernel { public: ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs, @@ -32,17 +55,24 @@ class ReshapeOp : public framework::OperatorWithKernel { PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of ReshapeOp should not be null."); - const std::vector &shape = ctx->Attrs().Get>("shape"); - PADDLE_ENFORCE(!shape.empty(), - "The shape information must be set by Attr(shape)."); + if (ctx->HasInputs("ShapeTensor")) { + // top prority shape + auto inputs_name = ctx->Inputs("ShapeTensor"); + PADDLE_ENFORCE(inputs_name.size() > 0, "shape tensor size can't be zero"); + auto out_dims = std::vector(inputs_name.size(), -1); + ctx->SetOutputDim("Out", framework::make_ddim(out_dims)); + return; + } if (ctx->HasInput("Shape") && ctx->IsRuntime()) { // If true, set the shape of Output(Out) according to Input(Shape) in // ReshapeKernel with ExecutionContext. Also check LoD in ReshapeKernel. ctx->ShareLoD("X", /*->*/ "Out"); return; } - + const std::vector &shape = ctx->Attrs().Get>("shape"); + PADDLE_ENFORCE(!shape.empty(), + "The shape information must be set by Attr(shape)."); auto x_dims = ctx->GetInputDim("X"); auto out_dims = ValidateShape(shape, x_dims); ctx->SetOutputDim("Out", out_dims); @@ -114,6 +144,16 @@ class ReshapeOp : public framework::OperatorWithKernel { return framework::OpKernelType(ctx.Input("X")->type(), ctx.device_context()); } + + framework::OpKernelType GetKernelTypeForVar( + const std::string &var_name, const Tensor &tensor, + const framework::OpKernelType &expected_kernel_type) const override { + if (var_name == "ShapeTensor") { + return expected_kernel_type; + } + return framework::OpKernelType(expected_kernel_type.data_type_, + tensor.place(), tensor.layout()); + } }; class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker { @@ -126,9 +166,18 @@ class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker { "the shape attribute, while the shape attribute still should be " "set correctly to gurantee shape inference in compile time.") .AsDispensable(); + AddInput( + "ShapeTensor", + "(vector>, optional). If provided, reshape will use this" + "The shape of the tensor in vector MUST BE [1]" + "it has the highest priority compare with Input(Shape) and " + "attr(shape).") + .AsDuplicable() + .AsDispensable(); AddOutput("Out", "(Tensor). The output tensor of reshape operator."); AddAttr>( - "shape", "(std::vector) Target shape of reshape operator."); + "shape", "(std::vector) Target shape of reshape operator.") + .SetDefault({}); AddComment(R"DOC( Reshape Operator. @@ -202,24 +251,35 @@ class ReshapeKernel { auto *out = ctx.Output("Out"); auto *in = ctx.Input("X"); - auto *shape_tensor = ctx.HasInput("Shape") - ? ctx.Input("Shape") - : nullptr; - framework::DDim out_dims = out->dims(); - if (shape_tensor) { - auto *shape_data = shape_tensor->data(); - framework::Tensor cpu_shape_tensor; - if (platform::is_gpu_place(shape_tensor->place())) { - TensorCopySync(*shape_tensor, platform::CPUPlace(), &cpu_shape_tensor); - shape_data = cpu_shape_tensor.data(); + auto list_new_shape_tensor = + ctx.MultiInput("ShapeTensor"); + if (list_new_shape_tensor.size() > 0) { + // have shape tensor + auto new_shape = get_new_shape(list_new_shape_tensor); + out_dims = ReshapeOp::ValidateShape(new_shape, in->dims()); + + } else { + auto *shape_tensor = ctx.HasInput("Shape") + ? ctx.Input("Shape") + : nullptr; + + if (shape_tensor) { + auto *shape_data = shape_tensor->data(); + framework::Tensor cpu_shape_tensor; + if (platform::is_gpu_place(shape_tensor->place())) { + TensorCopySync(*shape_tensor, platform::CPUPlace(), + &cpu_shape_tensor); + shape_data = cpu_shape_tensor.data(); + } + auto shape = + std::vector(shape_data, shape_data + shape_tensor->numel()); + out_dims = ReshapeOp::ValidateShape(shape, in->dims()); } - auto shape = - std::vector(shape_data, shape_data + shape_tensor->numel()); - out_dims = ReshapeOp::ValidateShape(shape, in->dims()); } + out->Resize(out_dims); out->mutable_data(ctx.GetPlace(), in->type()); framework::TensorCopy( *in, ctx.GetPlace(), @@ -288,6 +348,7 @@ class Reshape2GradMaker : public framework::SingleGradOpDescMaker { auto *grad_op = new framework::OpDesc(); grad_op->SetType("reshape2_grad"); grad_op->SetInput("XShape", Output("XShape")); + grad_op->SetInput("ShapeTensor", Input("ShapeTensor")); grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out")); grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X")); grad_op->SetAttrMap(Attrs()); @@ -320,6 +381,16 @@ class Reshape2GradOp : public framework::OperatorWithKernel { ctx.Input(framework::GradVarName("Out"))->type(), ctx.device_context()); } + + framework::OpKernelType GetKernelTypeForVar( + const std::string &var_name, const Tensor &tensor, + const framework::OpKernelType &expected_kernel_type) const override { + if (var_name == "ShapeTensor") { + return expected_kernel_type; + } + return framework::OpKernelType(expected_kernel_type.data_type_, + tensor.place(), tensor.layout()); + } }; class ReshapeOpInplaceInToOut : public framework::InplaceOpInference { diff --git a/paddle/fluid/operators/scatter.cu.h b/paddle/fluid/operators/scatter.cu.h index b2e79f6c82bb748293f4219845e6798347c8c46e..ce4af44266ee3b89c09007e8e1157987f2951279 100644 --- a/paddle/fluid/operators/scatter.cu.h +++ b/paddle/fluid/operators/scatter.cu.h @@ -13,7 +13,10 @@ See the License for the specific language governing permissions and limitations under the License. */ #pragma once +#include +#include "math/math_function.h" #include "paddle/fluid/framework/tensor.h" +#include "paddle/fluid/platform/cuda_primitives.h" #include "paddle/fluid/platform/place.h" namespace paddle { @@ -24,17 +27,33 @@ using Tensor = framework::Tensor; #define CUDA_1D_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ i += blockDim.x * gridDim.x) +template +__global__ void ScatterInitCUDAKernel(const IndexT* indices, T* output, + size_t index_size, size_t slice_size, + bool overwrite) { + CUDA_1D_KERNEL_LOOP(i, index_size * slice_size) { + int indices_i = i / slice_size; + int slice_i = i - indices_i * slice_size; // offset inside the slice + IndexT scatter_i = indices[indices_i]; + IndexT out_i = scatter_i * slice_size + slice_i; + *(output + out_i) = static_cast(0); + } +} -template -__global__ void ScatterCUDAKernel(const T* params, const int* indices, +template +__global__ void ScatterCUDAKernel(const T* params, const IndexT* indices, T* output, size_t index_size, - size_t slice_size) { + size_t slice_size, bool overwrite) { CUDA_1D_KERNEL_LOOP(i, index_size * slice_size) { int indices_i = i / slice_size; int slice_i = i - indices_i * slice_size; // offset inside the slice - int scatter_i = indices[indices_i]; - int out_i = scatter_i * slice_size + slice_i; - *(output + out_i) = *(params + i); + IndexT scatter_i = indices[indices_i]; + IndexT out_i = scatter_i * slice_size + slice_i; + if (overwrite) { + *(output + out_i) = *(params + i); + } else { + paddle::platform::CudaAtomicAdd(output + out_i, *(params + i)); + } } } @@ -43,14 +62,17 @@ __global__ void ScatterCUDAKernel(const T* params, const int* indices, * Return a new updated tensor from source tensor, scatter-assigned according to * index * input[src]: type-T source Tensor - * input[index]: type-int index Tensor (1-D) + * input[index]: type-IndexT index Tensor (1-D) * return: output tensor */ -template -void GPUScatterAssign(const platform::DeviceContext& ctx, const Tensor& src, - const Tensor& index, Tensor* output) { +template +void GPUScatterAssign(const framework::ExecutionContext& context, + const Tensor& src, const Tensor& index, Tensor* output, + bool overwrite = true) { // PADDLE_ENFORCE(platform::is_gpu_place(place)); // check index of shape 1-D + + const auto& ctx = context.device_context(); PADDLE_ENFORCE(index.dims().size() == 1 || (index.dims().size() == 2 && index.dims()[1] == 1)); int index_size = index.dims()[0]; @@ -64,17 +86,27 @@ void GPUScatterAssign(const platform::DeviceContext& ctx, const Tensor& src, for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; const T* p_src = src.data(); - const int* p_index = index.data(); + const IndexT* p_index = index.data(); T* p_output = output->data(); + const size_t& slice_bytes = slice_size * sizeof(T); + // set block and grid num int block = 512; int n = slice_size * index_size; int grid = (n + block - 1) / block; - ScatterCUDAKernel<<< + // if not overwrite mode, init data + if (!overwrite) { + ScatterInitCUDAKernel<<< + grid, block, 0, + reinterpret_cast(ctx).stream()>>>( + p_index, p_output, index_size, slice_size, overwrite); + } + + ScatterCUDAKernel<<< grid, block, 0, reinterpret_cast(ctx).stream()>>>( - p_src, p_index, p_output, index_size, slice_size); + p_src, p_index, p_output, index_size, slice_size, overwrite); } } // namespace operators diff --git a/paddle/fluid/operators/scatter.h b/paddle/fluid/operators/scatter.h index 8bae6606c94620ab4fa8ae34f69236e7e87e9670..680dc282c14b97c13c4d1df8275a790b2ba5a0d7 100644 --- a/paddle/fluid/operators/scatter.h +++ b/paddle/fluid/operators/scatter.h @@ -14,11 +14,14 @@ limitations under the License. */ #pragma once #include +#include #include "paddle/fluid/framework/ddim.h" #include "paddle/fluid/framework/eigen.h" #include "paddle/fluid/framework/tensor.h" +#include "paddle/fluid/operators/math/blas.h" #include "paddle/fluid/platform/place.h" +#include "unordered_set" namespace paddle { namespace operators { @@ -26,13 +29,48 @@ namespace operators { using Tensor = framework::Tensor; /** - * Return a updated tensor from source tensor, scattered according to index: + * Return the updated array pointer, use blas or eigen lib to optimize time + * cost + */ +template +typename std::enable_if::value>::type +elementwise_inner_add(const framework::ExecutionContext& ctx, + const T* src_pointer, const T* dist_pointer, + T* result_dist_pointer, const framework::Tensor& src, + framework::Tensor* dist, const int& src_index, + const IndexT& dist_index, const int& slice_size, + const size_t& slice_bytes) { + auto blas = math::GetBlas(ctx); + + blas.VADD(slice_size, src_pointer + src_index * slice_size, + dist_pointer + dist_index * slice_size, + result_dist_pointer + dist_index * slice_size); +} + +template +typename std::enable_if::value>::type +elementwise_inner_add(const framework::ExecutionContext& ctx, + const T* src_pointer, const T* dist_pointer, + T* result_dist_pointer, const framework::Tensor& src, + framework::Tensor* dist, const int& src_index, + const IndexT& dist_index, const int& slice_size, + const size_t& slice_bytes) { + auto src_slice = src.Slice(src_index, src_index + 1); + auto dist_slice = dist->Slice(dist_index, dist_index + 1); + + auto eigen_src = framework::EigenVector::Flatten(src_slice); + auto eigen_dist = framework::EigenVector::Flatten(dist_slice); + + eigen_dist += eigen_src; +} +/** + * Return an updated tensor from source tensor, scattered according to index: * dst[i] = src[index[i]] * input[src]: type-T source Tensor - * input[index]: type-int index Tensor (1-D) + * input[index]: type-IndexT index Tensor (1-D) * return: output tensor */ -template +template void ScatterAssign(const platform::DeviceContext& ctx, const Tensor& src, const Tensor& index, Tensor* output) { PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace())); @@ -45,7 +83,7 @@ void ScatterAssign(const platform::DeviceContext& ctx, const Tensor& src, auto dst_dims = output->dims(); const T* p_src = src.data(); - const int* p_index = index.data(); + const IndexT* p_index = index.data(); T* p_output = output->data(); // check src shape and dst shape should match @@ -59,10 +97,52 @@ void ScatterAssign(const platform::DeviceContext& ctx, const Tensor& src, const size_t slice_bytes = slice_size * sizeof(T); for (int i = 0; i < index_size; ++i) { - int index_ = p_index[i]; + IndexT index_ = p_index[i]; memcpy(p_output + index_ * slice_size, p_src + i * slice_size, slice_bytes); } } +template +void ScatterAssignAdd(const framework::ExecutionContext& ctx, const Tensor& src, + const Tensor& index, Tensor* output) { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.device_context().GetPlace())); + // check index of shape 1-D + PADDLE_ENFORCE(index.dims().size() == 1 || + (index.dims().size() == 2 && index.dims()[1] == 1)); + int index_size = index.dims()[0]; + + auto src_dims = src.dims(); + auto dst_dims = output->dims(); + + const T* p_src = src.data(); + const IndexT* p_index = index.data(); + + const T* p_output = output->data(); + T* result_p_output = output->data(); + + // check src shape and dst shape should match + for (int i = 1; i < src_dims.size(); i++) + PADDLE_ENFORCE(src_dims[i] == dst_dims[i]); + + // slice size + size_t slice_size = 1; + for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; + + const size_t& slice_bytes = slice_size * sizeof(T); + + // if not in overwrite mode, need to init output data + for (int i = 0; i < index_size; ++i) { + const IndexT& index_ = p_index[i]; + memset(result_p_output + slice_size * index_, 0, slice_bytes); + } + + for (int i = 0; i < index_size; ++i) { + const IndexT& index_ = p_index[i]; + elementwise_inner_add(ctx, p_src, p_output, result_p_output, src, + output, i, index_, slice_size, + slice_bytes); + } +} + } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/scatter_op.cc b/paddle/fluid/operators/scatter_op.cc index 68ad223b3c311bec5968eb18b50f15e9da84e6d3..f5a1b32e5c240933d79a524937b5a8222118fdd9 100644 --- a/paddle/fluid/operators/scatter_op.cc +++ b/paddle/fluid/operators/scatter_op.cc @@ -80,6 +80,14 @@ class ScatterOpMaker : public framework::OpProtoAndCheckerMaker { AddInput("Ids", "The index input of scatter op where X will be updated"); AddInput("Updates", "The updated value of scatter op"); AddOutput("Out", "The output of scatter op"); + AddAttr("overwrite", + "(bool, defalut: True) " + "The mode that updating the output when has same index," + "If True, use the overwrite mode to update the output" + "of the same index, if False, use the accumulate mode to" + "update the output of the same index,Default value is True." + "You can set overwrite=False to implement scatter_add.") + .SetDefault(true); AddComment(R"DOC( Scatter Operator. diff --git a/paddle/fluid/operators/scatter_op.cu b/paddle/fluid/operators/scatter_op.cu index a70b9091727935ddcbb83dd5775729969f7d64e5..e9ad347538157342adb24813546e927040b4f9d2 100644 --- a/paddle/fluid/operators/scatter_op.cu +++ b/paddle/fluid/operators/scatter_op.cu @@ -30,10 +30,10 @@ class ScatterOpCUDAKernel : public framework::OpKernel { auto *Ids = ctx.Input("Ids"); auto *Updates = ctx.Input("Updates"); auto *Out = ctx.Output("Out"); + bool overwrite = ctx.Attr("overwrite"); Out->ShareDataWith(*X); - - GPUScatterAssign(ctx.device_context(), *Updates, *Ids, Out); + GPUScatterAssign(ctx, *Updates, *Ids, Out, overwrite); } }; diff --git a/paddle/fluid/operators/scatter_op.h b/paddle/fluid/operators/scatter_op.h index 2eefbba9726af4d38b40d91e9242faa2923dca20..9c237dc0f1f115ce76a3b982a8c6ca1dfccb0b87 100644 --- a/paddle/fluid/operators/scatter_op.h +++ b/paddle/fluid/operators/scatter_op.h @@ -33,11 +33,33 @@ class ScatterOpKernel : public framework::OpKernel { auto *Ids = ctx.Input("Ids"); auto *Updates = ctx.Input("Updates"); auto *Out = ctx.Output("Out"); + double overwrite = ctx.Attr("overwrite"); // In place output: Out = X, Out[Ids] = Updates framework::TensorCopySync(*X, ctx.GetPlace(), Out); // Apply ScatterUpdate: Out[index] = Updates[:] - ScatterAssign(ctx.device_context(), *Updates, *Ids, Out); + const auto &index_type = Ids->type(); + bool index_type_match = index_type == framework::proto::VarType::INT32 || + index_type == framework::proto::VarType::INT64; + PADDLE_ENFORCE( + index_type_match, + "Index holds the wrong type, it holds %s, but desires to be %s or %s", + paddle::framework::DataTypeToString(index_type), + paddle::framework::DataTypeToString(framework::proto::VarType::INT32), + paddle::framework::DataTypeToString(framework::proto::VarType::INT64)); + if (overwrite) { + if (index_type == framework::proto::VarType::INT32) { + ScatterAssign(ctx.device_context(), *Updates, *Ids, Out); + } else { + ScatterAssign(ctx.device_context(), *Updates, *Ids, Out); + } + } else { + if (index_type == framework::proto::VarType::INT32) { + ScatterAssignAdd(ctx, *Updates, *Ids, Out); + } else { + ScatterAssignAdd(ctx, *Updates, *Ids, Out); + } + } } }; diff --git a/paddle/fluid/operators/sequence_ops/sequence_pool_op.cc b/paddle/fluid/operators/sequence_ops/sequence_pool_op.cc index b4923571df95432d030d393a69d427f3ae17f298..f3193fdc55609ee0cc608367c654b9d506217b6c 100644 --- a/paddle/fluid/operators/sequence_ops/sequence_pool_op.cc +++ b/paddle/fluid/operators/sequence_ops/sequence_pool_op.cc @@ -57,6 +57,9 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker { "(string, default 'AVERAGE') the pooling pooltype of SequencePoolOp.") .SetDefault("AVERAGE") .InEnum({"AVERAGE", "SUM", "SQRT", "LAST", "FIRST", "MAX"}); + AddAttr("pad_value", + "(float, default 0.0) The value to pad for empty sequence.") + .SetDefault(0.0); AddComment(R"DOC( Sequence Pool Operator. @@ -69,6 +72,8 @@ It supports six pooling types: 5. FIRST: Out[i] = first instance in i-th sequence X[i] 6. MAX: $$Out[i] = max(X_i)$$ +and for the empty sequence Out[i] = attr(pad_value). + The following example explains how this works: For a mini-batch of 3 variable-length sentences, containing 2, 3, and 2 time-steps: diff --git a/paddle/fluid/operators/sequence_ops/sequence_pool_op.h b/paddle/fluid/operators/sequence_ops/sequence_pool_op.h index f2e4a55dee49664b2fc09813f6dba5f68aaf11d5..c32734808c39313fcf0a0e624d246f2e52838edf 100644 --- a/paddle/fluid/operators/sequence_ops/sequence_pool_op.h +++ b/paddle/fluid/operators/sequence_ops/sequence_pool_op.h @@ -32,6 +32,7 @@ class SequencePoolKernel : public framework::OpKernel { auto* in = context.Input("X"); auto* out = context.Output("Out"); std::string pooltype = context.Attr("pooltype"); + T pad_value = static_cast(context.Attr("pad_value")); auto dims = in->dims(); auto lod = in->lod(); @@ -58,8 +59,8 @@ class SequencePoolKernel : public framework::OpKernel { index->mutable_data(context.GetPlace()); } math::SequencePoolFunctor pool; - pool(context.template device_context(), pooltype, *in, out, - is_test, index); + pool(context.template device_context(), pooltype, pad_value, + *in, out, is_test, index); } }; diff --git a/paddle/fluid/operators/sequence_ops/sequence_reverse_op.h b/paddle/fluid/operators/sequence_ops/sequence_reverse_op.h index 39dad2311b2bcf29f808723caf7bfaef4c88cef2..14e4fc9b0dd0561a2c3630165f73234f35fa024d 100644 --- a/paddle/fluid/operators/sequence_ops/sequence_reverse_op.h +++ b/paddle/fluid/operators/sequence_ops/sequence_reverse_op.h @@ -14,6 +14,7 @@ #pragma once +#include #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/operators/math/algorithm.h" #include "paddle/fluid/platform/for_range.h" @@ -109,7 +110,6 @@ class SequenceReverseOpKernel : public framework::OpKernel { PADDLE_ENFORCE_EQ(x.lod().size(), 1, "SequenceReverse Op only support one level lod."); - auto &dev_ctx = ctx.template device_context(); const size_t *lod; size_t lod_count = x.lod()[0].size(); @@ -131,10 +131,24 @@ class SequenceReverseOpKernel : public framework::OpKernel { PADDLE_ENFORCE_NE(x_data, y_data, "SequenceReverse Op does not support in-place operation"); - SequenceReverseFunctor functor(x_data, y_data, lod, lod_count, - row_numel); - platform::ForRange for_range(dev_ctx, limit); - for_range(functor); + if (platform::is_cpu_place(ctx.GetPlace())) { + for (size_t idx = 0; idx < lod_count - 1; idx++) { + auto start_pos = lod[idx]; + auto end_pos = lod[idx + 1]; + for (auto pos = start_pos; pos < end_pos; pos++) { + auto cur_pos = end_pos - pos - 1 + start_pos; + std::memcpy(y_data + pos * row_numel, x_data + cur_pos * row_numel, + row_numel * sizeof(T)); + } + } + } else { + auto &dev_ctx = ctx.template device_context(); + + SequenceReverseFunctor functor(x_data, y_data, lod, lod_count, + row_numel); + platform::ForRange for_range(dev_ctx, limit); + for_range(functor); + } } }; diff --git a/paddle/fluid/operators/sequence_ops/sequence_slice_op.h b/paddle/fluid/operators/sequence_ops/sequence_slice_op.h index 146b5cc9b3c6fc7772b3af64657689fa13f87bf0..a07fc54090d755114b878623104a8ac14f8cce8d 100644 --- a/paddle/fluid/operators/sequence_ops/sequence_slice_op.h +++ b/paddle/fluid/operators/sequence_ops/sequence_slice_op.h @@ -135,7 +135,8 @@ class SequenceSliceGradOpKernel : public framework::OpKernel { } auto lod = in->lod(); - auto out_lod = out_grad->lod(); + // to avoid out_grad missing lod, compute lod again + auto out_lod = SequenceSliceLoD(*in, offset_data, length_data); if (x_grad) { x_grad->mutable_data(ctx.GetPlace()); diff --git a/paddle/fluid/operators/sequence_ops/sequence_unpad_op.h b/paddle/fluid/operators/sequence_ops/sequence_unpad_op.h index fe8ca41b698159a782547ce673a374d074d3b73d..70f26055b7cc0516b051fab2c8094752d5a9f9f1 100644 --- a/paddle/fluid/operators/sequence_ops/sequence_unpad_op.h +++ b/paddle/fluid/operators/sequence_ops/sequence_unpad_op.h @@ -33,7 +33,6 @@ class SequenceUnpadOpKernel : public framework::OpKernel { auto* x_t = ctx.Input("X"); auto* len_t = ctx.Input("Length"); auto* out_t = ctx.Output("Out"); - out_t->mutable_data(ctx.GetPlace()); const int64_t* seq_len_ptr = nullptr; if (platform::is_gpu_place(ctx.GetPlace())) { @@ -67,6 +66,9 @@ class SequenceUnpadOpKernel : public framework::OpKernel { } out_t->Resize(framework::make_ddim(out_dims_vec)); + // after set the lod of output, allocate the memory + out_t->mutable_data(ctx.GetPlace()); + int64_t padded_length = x_t->dims()[1]; math::UnpaddingLoDTensorFunctor()( ctx.template device_context(), *x_t, out_t, diff --git a/paddle/fluid/operators/slice_op.cc b/paddle/fluid/operators/slice_op.cc index 589c98e51e32bc9eb7d6ccfb721a6a5f091470cf..08b7bf3d1e9d08a89a804ccbb1e71198e5f9efd1 100644 --- a/paddle/fluid/operators/slice_op.cc +++ b/paddle/fluid/operators/slice_op.cc @@ -97,27 +97,27 @@ the start or end indices, it represents number of elements before the end of that dimension. If the value passed to start or end is larger than the n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended -to pass in INT_MAX. If axes are omitted, they are set to [0, ..., ndim-1]. +to pass in INT_MAX. The size of axes must be equal to starts\' and ends\'. Following examples will explain how slice works: - .. code-block:: text - - Cast1: - Given: - data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] - axes = [0, 1] - starts = [1, 0] - ends = [2, 3] - Then: - result = [ [5, 6, 7], ] - - Cast2: - Given: - data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] - starts = [0, 1] - ends = [-1, 1000] - Then: - result = [ [2, 3, 4], ] +.. code-block:: text + + Case1: + Given: + data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] + axes = [0, 1] + starts = [1, 0] + ends = [2, 3] + Then: + result = [ [5, 6, 7], ] + + Case2: + Given: + data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] + starts = [0, 1] + ends = [-1, 1000] + Then: + result = [ [2, 3, 4], ] )DOC"); } }; diff --git a/paddle/fluid/operators/softmax_with_cross_entropy_op.cc b/paddle/fluid/operators/softmax_with_cross_entropy_op.cc index 456f78d2022e471bf8d35be542b9cf8347a7a944..716826bf1566148d825c5ba901c2852fa356eebb 100644 --- a/paddle/fluid/operators/softmax_with_cross_entropy_op.cc +++ b/paddle/fluid/operators/softmax_with_cross_entropy_op.cc @@ -248,7 +248,6 @@ class SoftmaxGradMaker : public framework::SingleGradOpDescMaker { grad_op->SetType("softmax_with_cross_entropy_grad"); grad_op->SetInput("Label", Input("Label")); grad_op->SetInput("Softmax", Output("Softmax")); - grad_op->SetInput(framework::GradVarName("Softmax"), OutputGrad("Softmax")); grad_op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss")); grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits")); grad_op->SetAttrMap(Attrs()); diff --git a/paddle/fluid/operators/sum_op.cc b/paddle/fluid/operators/sum_op.cc index 1eb4076d64d096f1fe230d7a7be211746135e847..e6c8772642573f1a4f331e8f33a77b34de7646fe 100644 --- a/paddle/fluid/operators/sum_op.cc +++ b/paddle/fluid/operators/sum_op.cc @@ -111,7 +111,7 @@ class SumOp : public framework::OperatorWithKernel { "Input var[%s] should not be nullptr", x_vars_name[idx]); auto tensor = framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_vars[idx]); - if (tensor->numel() == 0) { + if (tensor->numel() <= 0 || (!tensor->IsInitialized())) { continue; } if (dtype == -1) { diff --git a/paddle/fluid/operators/sum_op.cu b/paddle/fluid/operators/sum_op.cu index 5cecb7e09e7db2f3e4f63037352d3ee2b182ac3d..790626a59d0cd19ba0ccf463b1b270e629617078 100644 --- a/paddle/fluid/operators/sum_op.cu +++ b/paddle/fluid/operators/sum_op.cu @@ -126,12 +126,20 @@ void SumToLoDTensor(const framework::ExecutionContext &context) { auto &in_1 = in_vars[1]->Get(); auto length = in_0.numel(); - if (length) { + if (length && in_0.IsInitialized() && in_1.IsInitialized()) { auto result = EigenVector::Flatten(*out); auto &place = *dev_ctx.eigen_device(); auto in_0_e = EigenVector::Flatten(in_0); auto in_1_e = EigenVector::Flatten(in_1); result.device(place) = in_0_e + in_1_e; + } else if (length && in_0.IsInitialized()) { + auto result = EigenVector::Flatten(*out); + auto &place = *dev_ctx.eigen_device(); + result.device(place) = EigenVector::Flatten(in_0); + } else if (length && in_1.IsInitialized()) { + auto result = EigenVector::Flatten(*out); + auto &place = *dev_ctx.eigen_device(); + result.device(place) = EigenVector::Flatten(in_1); } return; } diff --git a/paddle/fluid/operators/tensorrt/tensorrt_engine_op.h b/paddle/fluid/operators/tensorrt/tensorrt_engine_op.h index 7f470924b337d59943c04ab0ff2820555f961732..21cf15cb0b04623a546a2c3ceb1050098daa938b 100644 --- a/paddle/fluid/operators/tensorrt/tensorrt_engine_op.h +++ b/paddle/fluid/operators/tensorrt/tensorrt_engine_op.h @@ -43,15 +43,16 @@ class TensorRTEngineOp : public framework::OperatorBase { private: std::vector input_names_; std::unordered_set param_names_; - mutable std::unique_ptr trt_engine_; + mutable TensorRTEngine *trt_engine_{nullptr}; int max_batch_size_; int workspace_size_; std::unique_ptr calibrator_; bool enable_int8_; + bool use_calib_mode_; std::string calibration_data_; std::string engine_key_; - std::string engine_serialized_data_; bool calibration_mode_; + int predictor_id_; int device_id_; public: @@ -65,9 +66,10 @@ class TensorRTEngineOp : public framework::OperatorBase { workspace_size_ = Attr("workspace_size"); device_id_ = Attr("gpu_id"); enable_int8_ = Attr("enable_int8"); + use_calib_mode_ = Attr("use_calib_mode"); calibration_data_ = Attr("calibration_data"); engine_key_ = Attr("engine_key"); - engine_serialized_data_ = Attr("engine_serialized_data"); + predictor_id_ = Attr("predictor_id"); auto params = Attr>("parameters"); for (const auto ¶m : params) { @@ -75,22 +77,21 @@ class TensorRTEngineOp : public framework::OperatorBase { } // calibration_mode is ture represents we need to // generate the calibration table data. - calibration_mode_ = (enable_int8_ && calibration_data_.size() == 0); + calibration_mode_ = + (enable_int8_ && calibration_data_.size() == 0 && use_calib_mode_); VLOG(4) << "calibration_mode: " << calibration_mode_; if (enable_int8_ && calibration_data_.size()) { calibrator_.reset(new TRTInt8Calibrator(calibration_data_)); } - - if (!calibration_mode_ && !engine_serialized_data_.empty()) { - trt_engine_.reset(new inference::tensorrt::TensorRTEngine( - max_batch_size_, workspace_size_, enable_int8_, calibrator_.get(), - device_id_)); - PADDLE_ENFORCE(engine_serialized_data_.size(), - "TRT serialized data should not be empty here," - "there must be error when generate serialized data in TRT " - "subgraph detect pass."); - trt_engine_->Deserialize(engine_serialized_data_); + bool has_engine = + inference::Singleton::Global() + .Has(engine_key_ + std::to_string(predictor_id_)); + + if (!calibration_mode_ && has_engine) { + trt_engine_ = + inference::Singleton::Global() + .Get(engine_key_ + std::to_string(predictor_id_)); } } @@ -236,12 +237,14 @@ class TensorRTEngineOp : public framework::OperatorBase { TensorRTEngine *GetEngine(const framework::Scope &scope, const platform::Place &dev_place) const { if (!trt_engine_) { - trt_engine_.reset(new inference::tensorrt::TensorRTEngine( - max_batch_size_, workspace_size_, enable_int8_, calibrator_.get(), - device_id_)); - PrepareTRTEngine(scope, trt_engine_.get()); + trt_engine_ = + inference::Singleton::Global() + .Create(engine_key_ + std::to_string(predictor_id_), + max_batch_size_, workspace_size_, enable_int8_, + calibrator_.get(), device_id_); + PrepareTRTEngine(scope, trt_engine_); } - return trt_engine_.get(); + return trt_engine_; } void PrepareTRTEngine(const framework::Scope &scope, diff --git a/paddle/fluid/operators/tensorrt/tensorrt_engine_op_test.cc b/paddle/fluid/operators/tensorrt/tensorrt_engine_op_test.cc index cc4d8d6e6f7e24dcb04ed0f58e63cb13ce176bdb..efc50fc06f4f86bd7f6ae9e832febfa9cbd2245b 100644 --- a/paddle/fluid/operators/tensorrt/tensorrt_engine_op_test.cc +++ b/paddle/fluid/operators/tensorrt/tensorrt_engine_op_test.cc @@ -102,8 +102,10 @@ TEST(TensorRTEngineOp, manual) { engine_op_desc.SetAttr("workspace_size", static_cast(1 << 20)); engine_op_desc.SetAttr("parameters", std::vector({})); engine_op_desc.SetAttr("engine_key", std::string("a_engine")); + engine_op_desc.SetAttr("predictor_id", 1); engine_op_desc.SetAttr("calibration_data", std::string("")); engine_op_desc.SetAttr("enable_int8", static_cast(false)); + engine_op_desc.SetAttr("use_calib_mode", static_cast(false)); engine_op_desc.SetAttr("output_name_mapping", std::vector({"z0"})); engine_op_desc.SetAttr("subgraph", std::string(block_->SerializeAsString())); @@ -200,8 +202,10 @@ void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) { engine_op_desc.SetAttr("parameters", std::vector({"y0", "y1", "y2", "y3"})); engine_op_desc.SetAttr("engine_key", std::string("b_engine")); + engine_op_desc.SetAttr("predictor_id", 1); engine_op_desc.SetAttr("calibration_data", std::string("")); engine_op_desc.SetAttr("enable_int8", static_cast(false)); + engine_op_desc.SetAttr("use_calib_mode", static_cast(false)); engine_op_desc.SetAttr("output_name_mapping", std::vector({"z3"})); engine_op_desc.SetAttr("subgraph", std::string(block_->SerializeAsString())); diff --git a/paddle/fluid/operators/unpool_op.h b/paddle/fluid/operators/unpool_op.h index 96abad3de9b959ee611355c67f1fa9e56c430b1b..e388ec5ae3937aadebdcd8ecce8d82dae05be7cd 100644 --- a/paddle/fluid/operators/unpool_op.h +++ b/paddle/fluid/operators/unpool_op.h @@ -61,10 +61,10 @@ class UnpoolGradKernel : public framework::OpKernel { auto& device_ctx = context.template device_context(); math::SetConstant zero; - if (in_x_grad) { - in_x_grad->mutable_data(context.GetPlace()); - zero(device_ctx, in_x_grad, static_cast(0)); - } + + in_x_grad->mutable_data(context.GetPlace()); + zero(device_ctx, in_x_grad, static_cast(0)); + math::Unpool2dMaxGradFunctor unpool2d_max_backward; unpool2d_max_backward(device_ctx, *in_x, *in_y, *out, *out_grad, in_x_grad); } diff --git a/paddle/fluid/operators/warpctc_op.cc b/paddle/fluid/operators/warpctc_op.cc index 217d400bb3c20b4b9e6117074cebbb35161017fd..deb5681f21076af5be28f53e8b31a4a1ba4b30ba 100644 --- a/paddle/fluid/operators/warpctc_op.cc +++ b/paddle/fluid/operators/warpctc_op.cc @@ -54,6 +54,15 @@ class WarpCTCOp : public framework::OperatorWithKernel { framework::LibraryType library_{framework::LibraryType::kPlain}; #ifdef PADDLE_WITH_CUDA if (platform::CanCUDNNBeUsed(ctx)) { +#if CUDA_VERSION >= 9000 + LOG(WARNING) + << "The cudnnCTCLoss of CUDNN7 have some diff between " + "CUDA9/CUDA10 and CUDA8. You can close use_cudnn option to " + "use " + "baidu-research/warp-ctc(https://github.com/baidu-research/" + "warp-ctc)"; +#endif + library_ = framework::LibraryType::kCUDNN; } #endif diff --git a/paddle/fluid/platform/CMakeLists.txt b/paddle/fluid/platform/CMakeLists.txt index c4386689d3e4fdf440f395eb586a28c2049b9403..5de00db55add1ebc0e7d81b14934a105fd3fe474 100644 --- a/paddle/fluid/platform/CMakeLists.txt +++ b/paddle/fluid/platform/CMakeLists.txt @@ -72,7 +72,7 @@ ENDIF() # avoiding cycle dependencies cc_library(device_context SRCS device_context.cc init.cc DEPS simple_threadpool malloc ${STREAM_CALLBACK_DEPS} place eigen3 stringpiece cpu_helper cpu_info framework_proto ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS} - temp_allocator ${dgc_deps} xxhash) + temp_allocator ${dgc_deps}) if(WIN32) if(WITH_GPU AND NOT WITH_DSO) diff --git a/paddle/fluid/platform/cudnn_desc.h b/paddle/fluid/platform/cudnn_desc.h index 4ed51acb587ba042f7e6ff54713854da449eb723..39a50b3bc99d5bd699afa8cf717e5962b792d1ae 100644 --- a/paddle/fluid/platform/cudnn_desc.h +++ b/paddle/fluid/platform/cudnn_desc.h @@ -183,15 +183,17 @@ class ConvolutionDescriptor { CUDNN_ENFORCE(dynload::cudnnSetConvolutionNdDescriptor( desc, pads.size(), pads.data(), strides.data(), dilations.data(), CUDNN_CROSS_CORRELATION, compute_type)); - CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType( - desc, CUDNN_DEFAULT_MATH)); #if CUDNN_VERSION_MIN(7, 0, 1) CUDNN_ENFORCE( platform::dynload::cudnnSetConvolutionGroupCount(desc, groups)); +#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1) + CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType( + desc, CUDNN_DEFAULT_MATH)); if (dtype == CUDNN_DATA_HALF) { CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType( desc, CUDNN_TENSOR_OP_MATH)); } +#endif #endif } diff --git a/paddle/fluid/platform/device_context.cc b/paddle/fluid/platform/device_context.cc index 61386bdf05ab4a5b11d94c942c4476abd8698714..4f048d44685a88c3342de48dc6f364c950605be9 100644 --- a/paddle/fluid/platform/device_context.cc +++ b/paddle/fluid/platform/device_context.cc @@ -167,8 +167,7 @@ class EigenCudaStreamDevice : public Eigen::StreamInterface { if (UNLIKELY(num_bytes == 0)) { return nullptr; } - auto buf = paddle::memory::Alloc(place_, num_bytes, - memory::Allocator::kScratchpad); + auto buf = paddle::memory::Alloc(place_, num_bytes); void* retv = buf->ptr(); { std::lock_guard lock(mtx_); @@ -232,8 +231,7 @@ void CudnnHolder::ReallocateWorkspace(size_t required_workspace_len) { PADDLE_ENFORCE(cudaStreamSynchronize(*stream_)); workspace_.reset(); } - workspace_ = paddle::memory::Alloc(place_, required_workspace_len, - paddle::memory::Allocator::kScratchpad); + workspace_ = paddle::memory::Alloc(place_, required_workspace_len); } CUDADeviceContext::CUDADeviceContext(CUDAPlace place) @@ -268,12 +266,14 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place) size_t cudnn_dso_ver = dynload::cudnnGetVersion(); LOG_FIRST_N(WARNING, 1) << "device: " << place_.device << ", cuDNN Version: " << cudnn_dso_ver / 1000 << "." - << (cudnn_dso_ver % 100) / 10 << "."; + << (cudnn_dso_ver % 1000) / 100 << "."; { // Check CUDA/CUDNN version compatiblity - auto local_cuda_version = runtime_version_ / 100; - auto compile_cuda_version = CUDA_VERSION / 100; + auto local_cuda_version = + (driver_version_ / 1000) * 10 + (driver_version_ % 100) / 10; + auto compile_cuda_version = + (CUDA_VERSION / 1000) * 10 + (CUDA_VERSION % 100) / 10; if (local_cuda_version < compile_cuda_version) { LOG_FIRST_N(WARNING, 1) << "WARNING: device: " << place_.device @@ -316,7 +316,9 @@ CUDADeviceContext::~CUDADeviceContext() { eigen_device_.reset(); PADDLE_ENFORCE(cudaStreamDestroy(stream_)); #if !defined(_WIN32) - PADDLE_ENFORCE(dynload::ncclCommDestroy(nccl_comm_)); + if (nccl_comm_) { + PADDLE_ENFORCE(dynload::ncclCommDestroy(nccl_comm_)); + } #endif } diff --git a/paddle/fluid/platform/dynload/cudnn.h b/paddle/fluid/platform/dynload/cudnn.h index 3008c166938d7db190e8f716ca925fda5ccebc25..67e2a18dd372243e5e7b5ddf128ec9e4e383484f 100644 --- a/paddle/fluid/platform/dynload/cudnn.h +++ b/paddle/fluid/platform/dynload/cudnn.h @@ -172,16 +172,19 @@ CUDNN_DNN_ROUTINE_EACH_R6(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP) #endif #if CUDNN_VERSION >= 7001 -#define CUDNN_DNN_ROUTINE_EACH_R7(__macro) \ - __macro(cudnnSetConvolutionGroupCount); \ - __macro(cudnnSetConvolutionMathType); \ - __macro(cudnnConvolutionBiasActivationForward); \ - __macro(cudnnCreateCTCLossDescriptor); \ - __macro(cudnnDestroyCTCLossDescriptor); \ - __macro(cudnnGetCTCLossDescriptor); \ - __macro(cudnnSetCTCLossDescriptor); \ - __macro(cudnnGetCTCLossWorkspaceSize); \ - __macro(cudnnCTCLoss); +#define CUDNN_DNN_ROUTINE_EACH_R7(__macro) \ + __macro(cudnnSetConvolutionGroupCount); \ + __macro(cudnnSetConvolutionMathType); \ + __macro(cudnnConvolutionBiasActivationForward); \ + __macro(cudnnCreateCTCLossDescriptor); \ + __macro(cudnnDestroyCTCLossDescriptor); \ + __macro(cudnnGetCTCLossDescriptor); \ + __macro(cudnnSetCTCLossDescriptor); \ + __macro(cudnnGetCTCLossWorkspaceSize); \ + __macro(cudnnCTCLoss); \ + __macro(cudnnGetConvolutionBackwardDataAlgorithm_v7); \ + __macro(cudnnGetConvolutionBackwardFilterAlgorithm_v7); \ + __macro(cudnnGetConvolutionForwardAlgorithm_v7); CUDNN_DNN_ROUTINE_EACH_R7(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP) #endif diff --git a/paddle/fluid/platform/dynload/dynamic_loader.cc b/paddle/fluid/platform/dynload/dynamic_loader.cc index 62f623b175e78ba7fc94ed9ab9a96b38f3d3e271..9aafc180b90c522ba8ee7508686279957ea97319 100644 --- a/paddle/fluid/platform/dynload/dynamic_loader.cc +++ b/paddle/fluid/platform/dynload/dynamic_loader.cc @@ -107,7 +107,8 @@ static inline void* GetDsoHandleFromDefaultPath(const std::string& dso_path, if (nullptr == dso_handle) { LOG(WARNING) << "Can not find library: " << dso_path - << ". Please try to add the lib path to LD_LIBRARY_PATH."; + << ". The process maybe hang. Please try to add the lib path " + "to LD_LIBRARY_PATH."; } return dso_handle; } diff --git a/paddle/fluid/platform/init.cc b/paddle/fluid/platform/init.cc index bb22628cdfbbb696bd503423f4c3fea0c3845f40..c4d16766c80ffc62dddfbe26361d6bb6158990c3 100644 --- a/paddle/fluid/platform/init.cc +++ b/paddle/fluid/platform/init.cc @@ -206,9 +206,6 @@ void InitGLOG(const std::string &prog_name) { // glog will not hold the ARGV[0] inside. // Use strdup to alloc a new string. google::InitGoogleLogging(strdup(prog_name.c_str())); -#ifndef _WIN32 - google::InstallFailureSignalHandler(); -#endif } #if defined(PADDLE_WITH_DGC) diff --git a/paddle/fluid/platform/lodtensor_printer.cc b/paddle/fluid/platform/lodtensor_printer.cc index 07eaf42d2d3bc20e7f7dc56bb0f4e0cc2fbac5e3..33d0fe6268046b3bcbd4addc75fcf34c03d70bf9 100644 --- a/paddle/fluid/platform/lodtensor_printer.cc +++ b/paddle/fluid/platform/lodtensor_printer.cc @@ -20,24 +20,6 @@ limitations under the License. */ namespace paddle { namespace platform { -template -void print_lod_tensor(const std::string& var_name, - const framework::LoDTensor& lod_tensor, - const std::string& print_info) { - auto inspect = lod_tensor.data(); - auto element_num = lod_tensor.numel(); - - std::ostringstream sstream; - sstream << print_info << "\t"; - sstream << var_name << "\t"; - sstream << inspect[0]; - for (int j = 1; j < element_num; ++j) { - sstream << " " << inspect[j]; - } - - std::cout << sstream.str() << std::endl; -} - void PrintVar(framework::Scope* scope, const std::string& var_name, const std::string& print_info) { framework::Variable* var = scope->FindVar(var_name); @@ -52,26 +34,11 @@ void PrintVar(framework::Scope* scope, const std::string& var_name, return; } - framework::LoDTensor printed_tensor; - printed_tensor.set_lod(tensor->lod()); - printed_tensor.Resize(tensor->dims()); - if (platform::is_cpu_place(tensor->place())) { - printed_tensor.ShareDataWith(*tensor); - } else { - platform::CPUPlace place; - framework::TensorCopy(*tensor, place, &printed_tensor); - } - -#define PrintLoDTensorCallback(cpp_type, proto_type) \ - do { \ - if (tensor->type() == proto_type) { \ - print_lod_tensor(var_name, printed_tensor, print_info); \ - return; \ - } \ - } while (0) - - _ForEachDataType_(PrintLoDTensorCallback); - VLOG(1) << "PrintVar: unrecognized data type:" << printed_tensor.type(); + std::ostringstream sstream; + sstream << print_info << "\t"; + sstream << var_name << "\t"; + sstream << *tensor << "\t"; + std::cout << sstream.str() << std::endl; } } // end namespace platform diff --git a/paddle/fluid/platform/mkldnn_reuse.h b/paddle/fluid/platform/mkldnn_reuse.h index ba3a82b4b07f4dcb3f0037e398c146ab167d7b57..f1fb6b156aedcbf4d834d53ebe4d443fd5f780d3 100644 --- a/paddle/fluid/platform/mkldnn_reuse.h +++ b/paddle/fluid/platform/mkldnn_reuse.h @@ -14,6 +14,7 @@ limitations under the License. */ #pragma once #include +#include #include #include #include "boost/optional.hpp" @@ -31,10 +32,13 @@ class MKLDNNHandler { public: MKLDNNHandler(const MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine, const std::string& base_key) - : dev_ctx_(dev_ctx), - engine_(engine), - key_(base_key), - is_reusing_(false) {} + : dev_ctx_(dev_ctx), engine_(engine), key_common_(base_key) { + // TODO(jczaja): Make it faster + auto tid = std::this_thread::get_id(); + std::stringstream ss; + ss << tid; + key_ = key_common_ + "-t:" + ss.str(); + } std::shared_ptr AcquireSrcMemory( const mkldnn::memory::desc& md, void* ptr) { @@ -73,16 +77,11 @@ class MKLDNNHandler { auto local_key = key_ + suffix; auto mem_p = std::static_pointer_cast(dev_ctx_.GetBlob(local_key)); - PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false), - "Fail to find mem primitive in device context"); if (mem_p == nullptr) { mem_p = std::make_shared(mdp, ptr); dev_ctx_.SetBlob(local_key, mem_p); } else { mem_p->set_data_handle(ptr); - // Mark that reusing happenned. All primitives from operator instance - // should be reused or none of them. So we check consistency - is_reusing_ = true; } return mem_p; } @@ -96,8 +95,6 @@ class MKLDNNHandler { auto local_key = key_ + suffix; auto mem_p = std::static_pointer_cast(dev_ctx_.GetBlob(local_key)); - PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false), - "Fail to find mem primitive in device context"); if (mem_p == nullptr) { // Call custom reorder/preprocessing func if available if (custom_func) { @@ -111,9 +108,6 @@ class MKLDNNHandler { dev_ctx_.SetBlob(local_key, mem_p); } else { mem_p->set_data_handle(ptr); - // Mark that reusing happenned. All primitives from operator instance - // should be reused or none of them. So we check consistency - is_reusing_ = true; } return mem_p; } @@ -155,8 +149,6 @@ class MKLDNNHandler { auto target_memory_p = std::static_pointer_cast(dev_ctx_.GetBlob(local_key)); - PADDLE_ENFORCE((target_memory_p != nullptr) || (is_reusing_ == false), - "Fail to find mem primitive in device context"); if (target_memory_p == nullptr) { target_memory_p = user_memory_p; std::shared_ptr reorder_p; @@ -187,7 +179,6 @@ class MKLDNNHandler { if (reorder_p != nullptr) { pipeline.push_back(*reorder_p); } - is_reusing_ = true; } return target_memory_p; } @@ -212,25 +203,29 @@ class MKLDNNHandler { dst_memory.reset(new mkldnn::memory(*dst_pd, to_void_cast(output_data))); } - static void AppendKey(std::string* key, - const mkldnn::memory::dims& input_dims, - const mkldnn::memory::dims& weights_dims, - const std::vector& strides, - const std::vector& paddings, - const std::vector& dilations, const int& groups, - const mkldnn::memory::data_type& srcdt, - const mkldnn::memory::format& format, const bool& relu, - const bool& residual, const std::string& suffix) { + static void AppendKey( + std::string* key, const mkldnn::memory::dims& input_dims, + const mkldnn::memory::dims& weights_dims, const std::vector& strides, + const std::vector& paddings, const std::vector& dilations, + const int& groups, const mkldnn::memory::data_type& srcdt, + const mkldnn::memory::format& format, const bool& relu, + const bool& residual, const bool& brelu, const std::string& suffix) { AppendKeyDims(key, input_dims); + AppendKeyDims(key, weights_dims); + AppendKeyVec(key, strides); + AppendKeyVec(key, paddings); + AppendKeyVec(key, dilations); + AppendKey(key, std::to_string(groups)); AppendKey(key, std::to_string(srcdt)); AppendKey(key, std::to_string(format)); AppendKey(key, std::to_string(relu)); AppendKey(key, std::to_string(residual)); + AppendKey(key, std::to_string(brelu)); AppendKey(key, suffix); } @@ -264,7 +259,7 @@ class MKLDNNHandler { const MKLDNNDeviceContext& dev_ctx_; mkldnn::engine engine_; std::string key_; - bool is_reusing_; + std::string key_common_; public: static constexpr int MaxKeyLength = 256; @@ -286,8 +281,6 @@ class TransposeMKLDNNHandler : public MKLDNNHandler { auto local_key = key_ + "@user_src_mem_p"; auto mem_p = std::static_pointer_cast(dev_ctx_.GetBlob(local_key)); - PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false), - " find mem primitive in device context"); if (mem_p == nullptr) { // Make memory descriptor using input format, unless it // cannot be trusted (nchw) then make up memory fmt manually @@ -303,9 +296,6 @@ class TransposeMKLDNNHandler : public MKLDNNHandler { dev_ctx_.SetBlob(local_key, mem_p); } else { mem_p->set_data_handle(ptr); - // Mark that reusing happenned. All primitives from operator instance - // should be reused or none of them. So we check consistency - is_reusing_ = true; } return mem_p; } @@ -315,23 +305,17 @@ class TransposeMKLDNNHandler : public MKLDNNHandler { auto local_key = key_ + "@user_dst_mem_p"; auto mem_p = std::static_pointer_cast(dev_ctx_.GetBlob(local_key)); - PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false), - " find mem primitive in device context"); if (mem_p == nullptr) { auto dst_mdp = mkldnn::memory::primitive_desc{ Axis2MemoryDesc(dims_, axis_), engine_}; - auto dst_data = output->mutable_data( - place, paddle::memory::Allocator::kDefault, dst_mdp.get_size()); + auto dst_data = output->mutable_data(place, dst_mdp.get_size()); mem_p = std::make_shared(dst_mdp, dst_data); dev_ctx_.SetBlob(local_key, mem_p); } else { auto dst_data = output->mutable_data(place); mem_p->set_data_handle(dst_data); - // Mark that reusing happenned. All primitives from operator instance - // should be reused or none of them. So we check consistency - is_reusing_ = true; } return mem_p; } @@ -342,14 +326,10 @@ class TransposeMKLDNNHandler : public MKLDNNHandler { auto prim_key = key_ + "@transpose_p"; auto transpose_p = std::static_pointer_cast(dev_ctx_.GetBlob(prim_key)); - PADDLE_ENFORCE((transpose_p != nullptr) || (is_reusing_ == false), - "Fail to find convolution primitive in device context"); if (transpose_p == nullptr) { transpose_p = std::make_shared(*(src_memory_p), *(dst_memory_p)); dev_ctx_.SetBlob(prim_key, transpose_p); - } else { - is_reusing_ = true; } return transpose_p; } @@ -396,6 +376,83 @@ class TransposeMKLDNNHandler : public MKLDNNHandler { std::vector logical_axis_; }; +class ReorderMKLDNNHandler : public MKLDNNHandler { + public: + ReorderMKLDNNHandler(std::vector& dims, // NOLINT + framework::proto::VarType::Type vtype, + mkldnn::memory::data_type dtype, + const platform::MKLDNNDeviceContext& dev_ctx, + mkldnn::engine engine, const std::string& base_key) + : platform::MKLDNNHandler(dev_ctx, engine, base_key), + dims_(dims), + vtype_(vtype), + dtype_(dtype) {} + + std::shared_ptr AcquireSrcMemory( + const mkldnn::memory::format& fmt, void* ptr) { + auto local_key = key_ + "@user_src_mem_p"; + auto mem_p = + std::static_pointer_cast(dev_ctx_.GetBlob(local_key)); + if (mem_p == nullptr) { + auto src_md = platform::MKLDNNMemDesc(dims_, dtype_, fmt); + mem_p = std::make_shared( + mkldnn::memory::primitive_desc{src_md, engine_}, ptr); + dev_ctx_.SetBlob(local_key, mem_p); + } else { + mem_p->set_data_handle(ptr); + } + return mem_p; + } + + std::shared_ptr AcquireDstMemory( + framework::Tensor* output, const mkldnn::memory::format& fmt, + platform::Place place) { + auto local_key = key_ + "@user_dst_mem_p"; + auto mem_p = + std::static_pointer_cast(dev_ctx_.GetBlob(local_key)); + if (mem_p == nullptr) { + auto dst_md = platform::MKLDNNMemDesc(dims_, dtype_, fmt); + auto dst_mdp = mkldnn::memory::primitive_desc{dst_md, engine_}; + + auto dst_data = output->mutable_data(place, vtype_); + + mem_p = std::make_shared(dst_mdp, dst_data); + dev_ctx_.SetBlob(local_key, mem_p); + } else { + auto dst_data = output->mutable_data(place, vtype_); + mem_p->set_data_handle(dst_data); + } + return mem_p; + } + + std::shared_ptr AcquireReorder( + std::shared_ptr dst_memory_p, + std::shared_ptr src_memory_p) { + auto prim_key = key_ + "@reorder_p"; + auto reorder_p = + std::static_pointer_cast(dev_ctx_.GetBlob(prim_key)); + if (reorder_p == nullptr) { + reorder_p = + std::make_shared(*(src_memory_p), *(dst_memory_p)); + dev_ctx_.SetBlob(prim_key, reorder_p); + } + return reorder_p; + } + + static std::string GetHash(std::vector& shape, // NOLINT + mkldnn::memory::format in_fmt, + mkldnn::memory::format out_fmt, + const std::string& suffix) { + return dims2str(shape) + std::to_string(in_fmt) + "->" + + std::to_string(out_fmt) + "#" + suffix; + } + + private: + std::vector dims_; + framework::proto::VarType::Type vtype_; + mkldnn::memory::data_type dtype_; +}; + template struct convolutional_algorithm; @@ -562,8 +619,9 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler { scale_data, mask); } - mkldnn::primitive_attr CreatePostOps(bool fuse_relu, - bool fuse_residual_conn = false) const { + mkldnn::primitive_attr CreatePostOps(bool fuse_relu, bool fuse_residual_conn, + bool fuse_brelu, + float fuse_brelu_threshold) const { mkldnn::primitive_attr conv_attr; mkldnn::post_ops post_operations; // Fusion with Elementwise layer relies on adding a sum post-operation with @@ -583,6 +641,14 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler { post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu, negative_slope, placeholder); } + + if (fuse_brelu) { + constexpr float scale = 1.0f; + constexpr float placeholder = 0.0f; + post_operations.append_eltwise(scale, + mkldnn::algorithm::eltwise_bounded_relu, + fuse_brelu_threshold, placeholder); + } conv_attr.set_post_ops(post_operations); return conv_attr; } @@ -594,36 +660,45 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler { const mkldnn::memory::desc& dst, const std::vector& strides, const std::vector& paddings, const mkldnn::engine& engine, const bool fuse_relu, const bool fuse_residual_conn, + const bool fuse_brelu, const float fuse_brelu_threshold, mkldnn::prop_kind fwd_prop_kind) { - const std::string key_conv_pd = key_ + "@conv_pd"; + // Conv PD has to be passed to Grad op that + // may be exxecuted by diffrent thread, hence + // for that one we use key that does not contain TID + const std::string key_conv_pd = key_common_ + "@conv_pd"; - auto conv_pd = std::static_pointer_cast( + conv_pd_ = std::static_pointer_cast( dev_ctx_.GetBlob(key_conv_pd)); - if (conv_pd == nullptr) { - mkldnn::memory::dims stride_dims = strides; - mkldnn::memory::dims padding_dims = paddings; - - auto conv_desc = - bias ? typename forward_t::desc( - fwd_prop_kind, convolutional_algorithm::T, src, - weights, *bias, dst, stride_dims, padding_dims, - padding_dims, mkldnn::padding_kind::zero) - : typename forward_t::desc( - fwd_prop_kind, convolutional_algorithm::T, src, - weights, dst, stride_dims, padding_dims, padding_dims, - mkldnn::padding_kind::zero); - - mkldnn::primitive_attr conv_attr = - CreatePostOps(fuse_relu, fuse_residual_conn); - - conv_pd_.reset( - new typename forward_t::primitive_desc(conv_desc, conv_attr, engine)); - // Save conv_pd/src_memory/weights_memory for backward pass - dev_ctx_.SetBlob(key_conv_pd, conv_pd_); - } else { - conv_pd_ = conv_pd; - is_reusing_ = true; + if (conv_pd_ == nullptr) { + static std::mutex acquire_barrier; + std::lock_guard block_threads_until_finish_this_job( + acquire_barrier); + + conv_pd_ = std::static_pointer_cast( + dev_ctx_.GetBlob(key_conv_pd)); + if (conv_pd_ == nullptr) { + mkldnn::memory::dims stride_dims = strides; + mkldnn::memory::dims padding_dims = paddings; + + auto conv_desc = + bias ? typename forward_t::desc( + fwd_prop_kind, convolutional_algorithm::T, + src, weights, *bias, dst, stride_dims, padding_dims, + padding_dims, mkldnn::padding_kind::zero) + : typename forward_t::desc( + fwd_prop_kind, convolutional_algorithm::T, + src, weights, dst, stride_dims, padding_dims, + padding_dims, mkldnn::padding_kind::zero); + + mkldnn::primitive_attr conv_attr = CreatePostOps( + fuse_relu, fuse_residual_conn, fuse_brelu, fuse_brelu_threshold); + + conv_pd_.reset(new typename forward_t::primitive_desc( + conv_desc, conv_attr, engine)); + // Save conv_pd/src_memory/weights_memory for backward pass + dev_ctx_.SetBlob(key_conv_pd, conv_pd_); + } } return conv_pd_; @@ -636,15 +711,11 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler { auto prim_key = key_ + "@conv_p"; auto conv_p = std::static_pointer_cast(dev_ctx_.GetBlob(prim_key)); - PADDLE_ENFORCE((conv_p != nullptr) || (is_reusing_ == false), - "Fail to find convolution primitive in device context"); if (conv_p == nullptr) { conv_p = std::make_shared(*conv_pd_, *src_memory_p, *weights_memory_p, *dst_memory_p); dev_ctx_.SetBlob(prim_key, conv_p); - } else { - is_reusing_ = true; } return conv_p; } @@ -657,16 +728,12 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler { auto prim_key = key_ + "@conv_p"; auto conv_p = std::static_pointer_cast(dev_ctx_.GetBlob(prim_key)); - PADDLE_ENFORCE((conv_p != nullptr) || (is_reusing_ == false), - "Fail to find convolution primitive in device context"); if (conv_p == nullptr) { conv_p = std::make_shared(*conv_pd_, *src_memory_p, *weights_memory_p, *bias_memory_p, *dst_memory_p); dev_ctx_.SetBlob(prim_key, conv_p); - } else { - is_reusing_ = true; } return conv_p; } @@ -678,17 +745,12 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler { auto prim_key = key_ + "@conv_bwd_weights_p"; auto conv_bwd_weights_p = std::static_pointer_cast( dev_ctx_.GetBlob(prim_key)); - PADDLE_ENFORCE( - (conv_bwd_weights_p != nullptr) || (is_reusing_ == false), - "Fail to find convolution bwd weights primitive in device context"); if (conv_bwd_weights_p == nullptr) { // create backward conv primitive for weights conv_bwd_weights_p = std::make_shared( *conv_bwd_weights_pd_, *src_memory_p, *diff_dst_memory_p, *diff_weights_memory_p); dev_ctx_.SetBlob(prim_key, conv_bwd_weights_p); - } else { - is_reusing_ = true; } return conv_bwd_weights_p; } @@ -700,20 +762,31 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler { auto prim_key = key_ + "@conv_bwd_data_p"; auto conv_bwd_data_p = std::static_pointer_cast(dev_ctx_.GetBlob(prim_key)); - PADDLE_ENFORCE( - (conv_bwd_data_p != nullptr) || (is_reusing_ == false), - "Fail to find convolution bwd data primitive in device context"); if (conv_bwd_data_p == nullptr) { conv_bwd_data_p = std::make_shared( *conv_bwd_data_pd_, *diff_dst_memory_p, *weights_memory_p, *diff_src_memory_p); dev_ctx_.SetBlob(prim_key, conv_bwd_data_p); - } else { - is_reusing_ = true; } return conv_bwd_data_p; } + // Generate keys for storing/retriving primitives for this operator + // TODO(jczaja): Make hashing function more optimial + static std::string GetHash(mkldnn::memory::dims& input_dims, // NOLINT + mkldnn::memory::dims& weights_dims, // NOLINT + const bool& fuse_relu, // NOLINT + const bool& fuse_brelu, // NOLINT + std::vector& strides, // NOLINT + std::vector& paddings, // NOLINT + std::vector& dilations, // NOLINT + int groups, const std::string& suffix) { + return dims2str(input_dims) + dims2str(weights_dims) + + std::to_string(fuse_relu) + std::to_string(fuse_brelu) + + dims2str(strides) + dims2str(paddings) + dims2str(dilations) + + std::to_string(groups) + suffix; + } + // Generate keys for storing/retriving primitives for this operator // TODO(jczaja): Make hashing function more optimial static std::string GetHash(mkldnn::memory::dims& input_dims, // NOLINT @@ -748,9 +821,8 @@ template static std::shared_ptr SetDstMemory( const framework::ExecutionContext& ctx, framework::Tensor* output, const std::shared_ptr& handler) { - T* output_data = output->mutable_data( - ctx.GetPlace(), ::paddle::memory::Allocator::kDefault, - handler->GetDstMemorySize()); + T* output_data = + output->mutable_data(ctx.GetPlace(), handler->GetDstMemorySize()); std::shared_ptr dst_memory_p = handler->AcquireDstMemoryFromPrimitive(to_void_cast(output_data)); return dst_memory_p; @@ -781,9 +853,8 @@ static void SetDstMemoryHandler( const framework::ExecutionContext& ctx, framework::Tensor* output, const std::shared_ptr& handler, std::shared_ptr* dst_memory_p) { - T* output_data = output->mutable_data( - ctx.GetPlace(), ::paddle::memory::Allocator::kDefault, - handler->GetDstMemorySize()); + T* output_data = + output->mutable_data(ctx.GetPlace(), handler->GetDstMemorySize()); (*dst_memory_p)->set_data_handle(to_void_cast(output_data)); } diff --git a/paddle/fluid/platform/nccl_helper.h b/paddle/fluid/platform/nccl_helper.h index b8b14b3d15efb47cbf53a393476f25158ebb5dff..d79ff6e2b98a3fb3722198b67785b41a83fcb7cd 100644 --- a/paddle/fluid/platform/nccl_helper.h +++ b/paddle/fluid/platform/nccl_helper.h @@ -124,8 +124,8 @@ struct NCCLContextMap { } else { rank = trainer_id; } - VLOG(3) << "init nccl rank: " << rank << " nranks: " << nranks - << " gpu id: " << gpu_id; + VLOG(1) << "init nccl rank:" << rank << ", nranks:" << nranks + << ", gpu_id:" << gpu_id << ", dev_id:" << order_[i]; PADDLE_ENFORCE(cudaSetDevice(gpu_id)); PADDLE_ENFORCE(platform::dynload::ncclCommInitRank( comms.get() + i, nranks, *nccl_id, rank)); @@ -160,6 +160,160 @@ struct NCCLContextMap { } }; +inline std::string GetFlatNCCLVarName(size_t pos) { + if (pos == 0) { + return NCCL_ID_VARNAME; + } + return string::Sprintf("%s_%d", NCCL_ID_VARNAME, static_cast(pos)); +} + +inline std::string GetHierarchicalExterNCCLVarName(size_t pos) { + return string::Sprintf("Hierarchical_exter_%s_%d", NCCL_ID_VARNAME, + static_cast(pos)); +} +inline std::string GetHierarchicalInterNCCLVarName(size_t pos) { + return string::Sprintf("Hierarchical_inter_%s_%d", NCCL_ID_VARNAME, + static_cast(pos)); +} + +class NCCLCommunicator { + public: + NCCLCommunicator() {} + virtual ~NCCLCommunicator() {} + + NCCLContextMap *DefaultFlatCtx() const { + if (flat_ctxs_.size() == 0) { + return nullptr; + } + + return flat_ctxs_[0].get(); + } + + std::vector> *GetFlatCtxs() { + return &flat_ctxs_; + } + + NCCLContextMap *GetFlatCtx(size_t run_order) const { + return flat_ctxs_[run_order % flat_ctxs_.size()].get(); + } + + NCCLContextMap *GetRunEnvNCCLCtx(size_t run_order, + bool use_hierarchical_allreduce) const { + if (!use_hierarchical_allreduce) { + return GetFlatCtx(run_order); + } + + return GetHierarchicalInterCtx(run_order); + } + + /* + *When nccl inits nccl comm using ncclCommInitAll, it meets error when + *allreduce ophandle and sync_batch_norm_op use ncclallreduce parallelly. So + *create a new nccl comm for sync_batch_norm_op. And these codes should be + *polished with a unified nccl management. + */ + NCCLContextMap *GetSyncBatchNormCtx( + framework::Scope *scope, const std::vector &places) { + auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME); + if (nccl_id_var != nullptr) { + return DefaultFlatCtx(); + } + + if (sync_batch_norm_ctx_.get() == nullptr) { + sync_batch_norm_ctx_.reset(new NCCLContextMap(places)); + } + return sync_batch_norm_ctx_.get(); + } + + void InitFlatCtxs(const std::vector &places, + const std::vector &nccl_ids, + size_t trainers_num, size_t trainer_id) { + if (nccl_ids.size() == 0) { + auto ptr = new platform::NCCLContextMap(places); + VLOG(1) << "init local trainer"; + flat_ctxs_.emplace_back(ptr); + return; + } + + for (size_t i = 0; i < nccl_ids.size(); i++) { + auto ptr = new platform::NCCLContextMap(places, nccl_ids[i], trainers_num, + trainer_id); + VLOG(1) << "init trainer_id:" << trainer_id << ", comm no:" << i; + flat_ctxs_.emplace_back(ptr); + } + } + + void InitHierarchicalCtxs(const std::vector &places, + const std::vector &inter_nccl_ids, + const std::vector &exter_nccl_ids, + size_t trainers_num, size_t trainer_id, + size_t inter_trainers_num, + size_t exter_trainers_num) { + PADDLE_ENFORCE(trainers_num == inter_trainers_num * exter_trainers_num, + "trainers_num:%llu != inter_trainers_num:%llu * " + "exter_trainers_num:%llu", + trainers_num, inter_trainers_num, exter_trainers_num); + + PADDLE_ENFORCE(inter_trainers_num > 1, "inter_trainers_num:%llu must > 1", + inter_trainers_num); + + int inter_trainer_id = trainer_id % inter_trainers_num; + for (size_t i = 0; i < inter_nccl_ids.size(); i++) { + VLOG(1) << "init inter_trainer_id:" << inter_trainer_id + << ", comm no:" << i; + auto local = new NCCLContextMap(places, inter_nccl_ids[i], + inter_trainers_num, inter_trainer_id); + + h_inter_ctxs_.emplace_back(local); + } + + int exter_trainer_id = -1; + if (trainer_id % inter_trainers_num == 0) { + exter_trainer_id = trainer_id / inter_trainers_num; + } + + if (exter_trainer_id >= 0) { + for (size_t i = 0; i < exter_nccl_ids.size(); i++) { + auto ex = new NCCLContextMap(places, exter_nccl_ids[i], + exter_trainers_num, exter_trainer_id); + VLOG(1) << "init exter_trainer_id:" << exter_trainer_id + << ", comm no:" << i; + h_exter_ctxs_.emplace_back(ex); + } + } + } + + bool NeedExterAllReduce() const { return h_exter_ctxs_.size() > 0; } + + NCCLContextMap *GetHierarchicalInterCtx(size_t run_order) const { + return h_inter_ctxs_[run_order % h_inter_ctxs_.size()].get(); + } + + NCCLContextMap *GetHierarchicalExterCtx(size_t run_order) const { + return h_exter_ctxs_[run_order % h_exter_ctxs_.size()].get(); + } + + std::vector> *GetHierarchicalInterCtxs() { + return &h_inter_ctxs_; + } + + std::vector> *GetHierarchicalExterCtxs() { + return &h_exter_ctxs_; + } + + protected: + // Support multi nccl comm on default nccl ring while NCCLContextMap can't. + std::vector> flat_ctxs_; + + // h_inter_ctxs_ and h_exter_ctxs_ are for 2d allreduce. + // And h_exter_ctxs_ can support multi comm too. + std::vector> h_inter_ctxs_; + std::vector> h_exter_ctxs_; + + // just used for sync_batch_norm op. + std::unique_ptr sync_batch_norm_ctx_; +}; + } // namespace platform } // namespace paddle #endif diff --git a/paddle/fluid/platform/ngraph_helper.h b/paddle/fluid/platform/ngraph_helper.h index 9e6521653b80abec1c5212f5deb84153335c2a9c..2bacd5bd4c368f7c97b8c2af2b5ada803d19d0bd 100644 --- a/paddle/fluid/platform/ngraph_helper.h +++ b/paddle/fluid/platform/ngraph_helper.h @@ -77,9 +77,7 @@ std::shared_ptr GetNode( std::unordered_map>> ngb_node_map) { auto& var_names = var_map.at(name); - PADDLE_ENFORCE_EQ(var_names.size(), 1, - "op %s name %s expects one associated var", op->Type(), - name); + if (var_names.size() == 0) return nullptr; if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) { return (*ngb_node_map)[var_names[0]]; } else { @@ -132,16 +130,6 @@ void SetOutputNode( ngb_node_map) { auto& var_names = op->Outputs().at(name); if (var_names.size() == 1) { - /* */ - auto dummy_out = GetOutputNode(op, name, ngb_node_map); - if (dummy_out && dummy_out->get_shape() != node->get_shape()) { - node = NgReshaper(node, dummy_out->get_shape()); - } - if (dummy_out && - dummy_out->get_element_type() != node->get_element_type()) { - node = std::make_shared( - node, dummy_out->get_element_type()); - } (*ngb_node_map)[var_names[0]] = node; } else if (var_names.size() == 0) { (*ngb_node_map)[""] = node; @@ -189,6 +177,22 @@ inline void TrimTrailingSingularDims(ngraph::Shape* shape) { } } } + +ngraph::element::Type GetNgType(paddle::framework::proto::VarType::Type dtype) { + ngraph::element::Type ng_dtype; + if (dtype == paddle::framework::proto::VarType::FP32) { + ng_dtype = ngraph::element::f32; + } else if (dtype == paddle::framework::proto::VarType::FP64) { + ng_dtype = ngraph::element::f64; + } else if (dtype == paddle::framework::proto::VarType::INT64) { + ng_dtype = ngraph::element::i64; + } else if (dtype == paddle::framework::proto::VarType::INT32) { + ng_dtype = ngraph::element::i32; + } else { + PADDLE_THROW("unsupported data type: %s", dtype); + } + return ng_dtype; +} } // namespace platform } // namespace paddle diff --git a/paddle/fluid/platform/temporary_allocator.cc b/paddle/fluid/platform/temporary_allocator.cc index d489ed5368ed95a1a0a8b0d6759310501cd49fcd..6177b024f0ccbeeae14106868e2fc5ca7b8789eb 100644 --- a/paddle/fluid/platform/temporary_allocator.cc +++ b/paddle/fluid/platform/temporary_allocator.cc @@ -14,7 +14,6 @@ #include "paddle/fluid/platform/temporary_allocator.h" #include -#include #include "paddle/fluid/memory/allocation/allocator_facade.h" DEFINE_int64(limit_of_tmp_allocation, -1, @@ -31,38 +30,31 @@ namespace paddle { namespace platform { namespace alloc = memory::allocation; -TemporaryAllocation::TemporaryAllocation( - alloc::AllocationPtr &&underlying_allocation) - : Allocation(underlying_allocation->ptr(), underlying_allocation->size(), - underlying_allocation->place()), - underlying_allocation_(std::move(underlying_allocation)) {} - TemporaryAllocator::TemporaryAllocator(platform::Place place) : place_(place) { - temp_mem_map_.reset(new std::multimap()); + temp_mem_map_.reset(new std::multimap()); } bool TemporaryAllocator::IsAllocThreadSafe() const { return true; } void TemporaryAllocator::Release(const std::function &callback) { - std::unique_ptr> t_allocations; + std::unique_ptr> t_allocations; { std::unique_lock lock(mtx_); callback(); t_allocations.swap(temp_mem_map_); - temp_mem_map_.reset(new std::multimap()); + temp_mem_map_.reset(new std::multimap()); wait_delete_mem_ = 0; } + alloc::AllocationDeleter deleter; for (auto tmp : *t_allocations) { VLOG(10) << "Delete temporary allocation " << tmp.second->ptr() << " size: " << tmp.second->size(); - delete tmp.second; + deleter(tmp.second); } } -void TemporaryAllocator::Free(alloc::Allocation *allocation) { - auto *temp_allocation = dynamic_cast(allocation); - PADDLE_ENFORCE_NOT_NULL(temp_allocation); +void TemporaryAllocator::FreeImpl(alloc::Allocation *temp_allocation) { if (platform::is_gpu_place(temp_allocation->place())) { PADDLE_ENFORCE(platform::is_same_place(temp_allocation->place(), place_), "The place should be the same."); @@ -77,8 +69,8 @@ void TemporaryAllocator::Free(alloc::Allocation *allocation) { << "wait_delete_mem: " << wait_delete_mem; } - if (FLAGS_limit_of_tmp_allocation > 0 && - wait_delete_mem > static_cast(FLAGS_limit_of_tmp_allocation)) { + if (FLAGS_limit_of_tmp_allocation >= 0 && + wait_delete_mem >= static_cast(FLAGS_limit_of_tmp_allocation)) { PADDLE_ENFORCE(callback_ != nullptr, "The callback is non-initialized."); Release(callback_); } @@ -86,7 +78,7 @@ void TemporaryAllocator::Free(alloc::Allocation *allocation) { } VLOG(10) << "Delete temporary allocation " << temp_allocation->ptr() << " size: " << temp_allocation->size(); - delete temp_allocation; + alloc::AllocationDeleter()(temp_allocation); } size_t TemporaryAllocator::TemporaryAllocationQueueSize() { @@ -98,8 +90,7 @@ void TemporaryAllocator::SetCallback(const std::function &callback) { callback_ = callback; } -alloc::Allocation *TemporaryAllocator::AllocateImpl( - size_t size, alloc::Allocator::Attr attr) { +alloc::Allocation *TemporaryAllocator::AllocateImpl(size_t size) { { // Find available allocation in temp_mem_map. std::unique_lock lock(mtx_); @@ -121,11 +112,9 @@ alloc::Allocation *TemporaryAllocator::AllocateImpl( } // If not find the the available allocation, get allocation from // AllocatorFacadeInstance. - auto raw_allocation = - alloc::AllocatorFacade::Instance().Alloc(place_, size, attr); - auto temp_mem = new TemporaryAllocation(std::move(raw_allocation)); + auto temp_mem = alloc::AllocatorFacade::Instance().Alloc(place_, size); VLOG(10) << "Alloc temporary allocation: " << temp_mem->ptr() << ": " << size; - return temp_mem; + return temp_mem.release(); } } // namespace platform diff --git a/paddle/fluid/platform/temporary_allocator.h b/paddle/fluid/platform/temporary_allocator.h index f8a43b889d58d5e027aac8e08324cf51b7d82913..41f0e4a80b735e6c4eabce864ac5a1dfe1d67ced 100644 --- a/paddle/fluid/platform/temporary_allocator.h +++ b/paddle/fluid/platform/temporary_allocator.h @@ -23,14 +23,6 @@ namespace paddle { namespace platform { -class TemporaryAllocation : public memory::allocation::Allocation { - public: - explicit TemporaryAllocation( - memory::allocation::AllocationPtr &&underlying_allocation); - - memory::allocation::AllocationPtr underlying_allocation_; -}; - /*! \brief the TemporaryAllocator is used to alloc the temporary allocation * which used by CUDA's async operation. * @@ -57,17 +49,16 @@ class TemporaryAllocator : public memory::allocation::Allocator { void SetCallback(const std::function &callback); protected: - void Free(memory::allocation::Allocation *allocation) override; + void FreeImpl(memory::allocation::Allocation *allocation) override; - memory::allocation::Allocation *AllocateImpl( - size_t size, memory::allocation::Allocator::Attr attr) override; + memory::allocation::Allocation *AllocateImpl(size_t size) override; private: platform::Place place_; // When the allocation is not held by any variable, it should be placed // to temp_mem_map immediately. - std::unique_ptr> temp_mem_map_{ - nullptr}; + std::unique_ptr> + temp_mem_map_{nullptr}; std::mutex mtx_; size_t wait_delete_mem_{0}; std::function callback_; diff --git a/paddle/fluid/platform/timer.h b/paddle/fluid/platform/timer.h index 56019ae7cf21c15c10b1f9247c9d95deb2a48c43..ff0e1d95c2946b6db3ac0c05acba64ff5d3c59ef 100644 --- a/paddle/fluid/platform/timer.h +++ b/paddle/fluid/platform/timer.h @@ -50,7 +50,7 @@ class Timer { struct timeval _start; struct timeval _now; int _count; - int _elapsed; + int64_t _elapsed; bool _paused; // get us difference between start and now diff --git a/paddle/fluid/pybind/CMakeLists.txt b/paddle/fluid/pybind/CMakeLists.txt index d709508a6d54c0b8d62da00b3bc9e6877c6652bf..eeee507110ce9fc4ba87be0ceffeefd7eb02e0a9 100644 --- a/paddle/fluid/pybind/CMakeLists.txt +++ b/paddle/fluid/pybind/CMakeLists.txt @@ -5,7 +5,29 @@ set(PYBIND_DEPS pybind python proto_desc memory executor async_executor fleet_wr if(WITH_PYTHON) list(APPEND PYBIND_DEPS py_func_op) endif() -set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc reader_py.cc async_executor_py.cc fleet_wrapper_py.cc nccl_wrapper_py.cc data_set_py.cc imperative.cc ir.cc inference_api.cc) + +if (WITH_DISTRIBUTE) + list(APPEND PYBIND_DEPS communicator) +endif() + +set(PYBIND_SRCS + pybind.cc + exception.cc + protobuf.cc + const_value.cc + recordio.cc + reader_py.cc + async_executor_py.cc + fleet_wrapper_py.cc + nccl_wrapper_py.cc + data_set_py.cc + imperative.cc + ir.cc + inference_api.cc) + +if (WITH_DISTRIBUTE) + list(APPEND PYBIND_SRCS communicator_py.cc) +endif() if(WITH_PYTHON) if(WITH_AMD_GPU) diff --git a/paddle/fluid/pybind/data_set_py.cc b/paddle/fluid/pybind/data_set_py.cc index 3f171b65ab83de5a0d84d3c29b1e82510bf69716..3e2c976076aa1e1760511c31f77cef132e116dd2 100644 --- a/paddle/fluid/pybind/data_set_py.cc +++ b/paddle/fluid/pybind/data_set_py.cc @@ -66,7 +66,9 @@ void BindDataset(py::module* m) { .def("load_into_memory", &framework::Dataset::LoadIntoMemory) .def("release_memory", &framework::Dataset::ReleaseMemory) .def("local_shuffle", &framework::Dataset::LocalShuffle) - .def("global_shuffle", &framework::Dataset::GlobalShuffle); + .def("global_shuffle", &framework::Dataset::GlobalShuffle) + .def("get_memory_data_size", &framework::Dataset::GetMemoryDataSize) + .def("get_shuffle_data_size", &framework::Dataset::GetShuffleDataSize); } } // end namespace pybind diff --git a/paddle/fluid/pybind/fleet_wrapper_py.cc b/paddle/fluid/pybind/fleet_wrapper_py.cc index 2f6a7d2480aedd5bd37d0dbd5ccf64447e4a21ff..d279ff3d9e40eb06b69d5aa970c54d22973e8b2f 100644 --- a/paddle/fluid/pybind/fleet_wrapper_py.cc +++ b/paddle/fluid/pybind/fleet_wrapper_py.cc @@ -47,12 +47,17 @@ void BindFleetWrapper(py::module* m) { .def("run_server", &framework::FleetWrapper::RunServer) .def("init_worker", &framework::FleetWrapper::InitWorker) .def("init_model", &framework::FleetWrapper::PushDenseParamSync) + .def("save_model", &framework::FleetWrapper::SaveModel) + .def("load_model", &framework::FleetWrapper::LoadModel) .def("stop_server", &framework::FleetWrapper::StopServer) .def("gather_servers", &framework::FleetWrapper::GatherServers) .def("gather_clients", &framework::FleetWrapper::GatherClients) .def("get_clients_info", &framework::FleetWrapper::GetClientsInfo) .def("create_client2client_connection", - &framework::FleetWrapper::CreateClient2ClientConnection); + &framework::FleetWrapper::CreateClient2ClientConnection) + .def("shrink_sparse_table", &framework::FleetWrapper::ShrinkSparseTable) + .def("shrink_dense_table", &framework::FleetWrapper::ShrinkDenseTable) + .def("client_flush", &framework::FleetWrapper::ClientFlush); } // end FleetWrapper } // end namespace pybind } // end namespace paddle diff --git a/paddle/fluid/pybind/imperative.cc b/paddle/fluid/pybind/imperative.cc index 265707f1bccdabd37b9a7248755d0b81339418c3..0d15b9a44d83130385044af038472b1570b8ac12 100644 --- a/paddle/fluid/pybind/imperative.cc +++ b/paddle/fluid/pybind/imperative.cc @@ -14,12 +14,18 @@ limitations under the License. */ #include "paddle/fluid/pybind/imperative.h" +#include #include #include #include #include +#include +#include +#include #include "paddle/fluid/framework/block_desc.h" +#include "paddle/fluid/imperative/layer.h" +#include "paddle/fluid/imperative/profiler.h" #include "paddle/fluid/imperative/tracer.h" #include "paddle/fluid/imperative/type_defs.h" @@ -28,77 +34,318 @@ limitations under the License. */ namespace paddle { namespace pybind { +namespace py = ::pybind11; + +class Layer : public imperative::Layer { + public: + using imperative::Layer::Layer; // Inherit constructors + + std::vector> Forward( + const std::vector> &inputs) + override { + PYBIND11_OVERLOAD(std::vector>, Layer, + Forward, + inputs); // NOLINT + } +}; + +class PYBIND11_HIDDEN PyOpBase : public imperative::OpBase { + public: + using imperative::OpBase::OpBase; // Inherit constructors + + PyOpBase(const std::string &name) : OpBase(name) {} +}; + +// Function like obj.attr_name in Python. +static PyObject *GetPythonAttribute(PyObject *obj, const char *attr_name) { + // NOTE(zjl): PyObject_GetAttrString would return nullptr when attr_name + // is not inside obj, but it would also set the error flag of Python. + // If the error flag is set in C++, C++ code would not raise Exception, + // but Python would raise Exception once C++ call ends. + // To avoid unexpected Exception raised in Python, we check whether + // attribute exists before calling PyObject_GetAttrString. + // + // Caution: PyObject_GetAttrString would increase reference count of PyObject. + // Developer should call Py_DECREF manually after the attribute is not used. + if (PyObject_HasAttrString(obj, attr_name)) { + return PyObject_GetAttrString(obj, attr_name); + } else { + return nullptr; + } +} + +template +static T PyObjectCast(PyObject *obj) { + try { + return py::cast(py::handle(obj)); + } catch (py::cast_error &) { + PADDLE_THROW("Python object is not type of %s", typeid(T).name()); + } +} + +// NOTE(zjl): py::handle is a very light wrapper of PyObject *. +// Unlike py::object, py::handle does not change reference count of PyObject *. +static std::vector> +GetVarBaseListFromPyHandle(const py::handle &handle) { + PyObject *py_obj = handle.ptr(); // get underlying PyObject + // Python None is not nullptr in C++! + if (!py_obj || py_obj == Py_None) { + return {}; + } + + const char *kIVarField = "_ivar"; + PyObject *py_ivar = GetPythonAttribute(py_obj, kIVarField); + std::vector> result; + + if (py_ivar) { // Variable + result.emplace_back( + PyObjectCast>(py_ivar)); + Py_DECREF(py_ivar); + } else if (PyList_Check(py_obj)) { // List of Variable + size_t len = PyList_GET_SIZE(py_obj); + result.reserve(len); + for (size_t i = 0; i < len; ++i) { + PyObject *py_ivar = + PyObject_GetAttrString(PyList_GET_ITEM(py_obj, i), kIVarField); + PADDLE_ENFORCE_NOT_NULL(py_ivar); + result.emplace_back( + PyObjectCast>(py_ivar)); + Py_DECREF(py_ivar); + } + } else if (PyTuple_Check(py_obj)) { // Tuple of Variable + size_t len = PyTuple_GET_SIZE(py_obj); + result.reserve(len); + for (size_t i = 0; i < len; ++i) { + PyObject *py_ivar = + PyObject_GetAttrString(PyTuple_GET_ITEM(py_obj, i), kIVarField); + PADDLE_ENFORCE_NOT_NULL(py_ivar); + result.emplace_back( + PyObjectCast>(py_ivar)); + Py_DECREF(py_ivar); + } + } else { + PADDLE_THROW( + "unsupported type %s, must be Variable, List[Variable] or " + "tuple[Variable]", + py::str(handle)); + } + + PADDLE_ENFORCE(PyErr_Occurred() == nullptr, + py::str(py::handle(PyErr_Occurred()))); + + return result; +} + +using PyVarBaseMap = std::unordered_map; + +static imperative::VarBasePtrMap ConvertToVarBasePtrMap( + const PyVarBaseMap &map) { + imperative::VarBasePtrMap result; + for (auto &pair : map) { + auto var_vec = GetVarBaseListFromPyHandle(pair.second); + if (!var_vec.empty()) { + result.emplace(pair.first, std::move(var_vec)); + } + } + return result; +} + // Bind Methods -void BindImperative(pybind11::module* m) { - pybind11::class_(*m, "Tracer", "") +void BindImperative(pybind11::module *m_ptr) { + auto &m = *m_ptr; + + py::class_ backward_strategy( + m, "BackwardStrategy", R"DOC( + + BackwardStrategy is a descriptor of a how to run the backward process. Now it has: + + 1. :code:`sort_sum_gradient`, which will sum the gradient by the reverse order of trace. + + Examples: + + .. code-block:: python + import numpy as np + import paddle.fluid as fluid + from paddle.fluid import FC + + x = np.ones([2, 2], np.float32) + with fluid.dygraph.guard(): + inputs2 = [] + for _ in range(10): + inputs2.append(fluid.dygraph.base.to_variable(x)) + ret2 = fluid.layers.sums(inputs2) + loss2 = fluid.layers.reduce_sum(ret2) + backward_strategy = fluid.dygraph.BackwardStrategy() + backward_strategy.sort_sum_gradient = True + loss2.backward(backward_strategy) + )DOC"); + backward_strategy.def(py::init()) + .def_property("sort_sum_gradient", + [](const imperative::detail::BackwardStrategy &self) { + return self.sorted_sum_gradient_; + }, + [](imperative::detail::BackwardStrategy &self, + bool sorted_sum_gradient) { + self.sorted_sum_gradient_ = sorted_sum_gradient; + }); + + m.def("start_imperative_gperf_profiler", + []() { imperative::StartProfile(); }); + + m.def("stop_imperative_gperf_profiler", []() { imperative::StopProfile(); }); + + m.def("_is_dygraph_debug_enabled", + []() { return imperative::IsDebugEnabled(); }); + m.def("_dygraph_debug_level", []() { return imperative::GetDebugLevel(); }); + + py::class_>( + m, "VarBase", R"DOC()DOC") + .def_static("_alive_vars", &imperative::VarBase::AliveVarNames) + .def( + py::init, const paddle::platform::CPUPlace, + bool, bool>()) + .def( + py::init, + const paddle::platform::CUDAPlace, bool, bool>()) + .def("_run_backward", + [](imperative::VarBase &self, + const imperative::detail::BackwardStrategy &bckst) { + self.RunBackward(bckst); + }) + .def("_grad_name", &imperative::VarBase::GradName) + .def("_grad_value", &imperative::VarBase::GradValue) + .def("_clear_gradient", &imperative::VarBase::ClearGradient) + .def("_grad_ivar", + [](const imperative::VarBase &self) { return self.grads_; }, + py::return_value_policy::reference) + .def("_copy_to", + [](const imperative::VarBase &self, const platform::CPUPlace &place, + bool blocking) { + return self.NewVarBase(place, blocking).release(); + }, + py::return_value_policy::take_ownership) + .def("_copy_to", + [](const imperative::VarBase &self, const platform::CUDAPlace &place, + bool blocking) { + return self.NewVarBase(place, blocking).release(); + }, + py::return_value_policy::take_ownership) + .def("value", + [](const imperative::VarBase &self) { return self.var_.get(); }, + py::return_value_policy::reference) + .def_property("name", &imperative::VarBase::Name, + &imperative::VarBase::SetName) + .def_property_readonly("shape", &imperative::VarBase::Shape) + .def_property_readonly("dtype", &imperative::VarBase::DataType) + .def_property("persistable", &imperative::VarBase::IsPersistable, + &imperative::VarBase::SetPersistable) + .def_property("stop_gradient", &imperative::VarBase::IsStopGradient, + &imperative::VarBase::SetStopGradient); + + py::class_(m, "OpBase", R"DOC()DOC") + .def(py::init()) + .def("register_backward_hooks", + [](imperative::OpBase &self, const py::object &callable) { + self.RegisterBackwardHooks(callable); + }) + .def_property("_trace_id", + [](const imperative::OpBase &self) { + py::gil_scoped_release release; + return self.trace_id_; + }, + [](imperative::OpBase &self, int trace_id) { + py::gil_scoped_release release; + self.trace_id_ = trace_id; + }, + py::return_value_policy::reference) + .def_property_readonly("type", &imperative::OpBase::Type); + + py::class_ layer(m, "Layer"); + layer.def(py::init<>()) + .def("forward", + [](imperative::Layer &self, + const std::vector> &inputs) { + return self.Forward(inputs); + }); + + // NOTE(zjl): Tracer use PyVarBaseMap as its parameter but not VarBasePtrMap. + // We call Python C-API to convert PyVarBaseMap to VarBasePtrMap, instead + // making conversion in Python code. This speed up Tracer.trace() about 6% + // in ptb model and make time cost in Python to be nearly zero. + py::class_(m, "Tracer", "") .def("__init__", - [](imperative::Tracer& self, framework::BlockDesc* root_block) { + [](imperative::Tracer &self, framework::BlockDesc *root_block) { new (&self) imperative::Tracer(root_block); }) .def("trace", - [](imperative::Tracer& self, imperative::OpBase* op, - const imperative::VarBasePtrMap& inputs, - imperative::VarBasePtrMap* outputs, + [](imperative::Tracer &self, imperative::OpBase *op, + const PyVarBaseMap &inputs, const PyVarBaseMap &outputs, framework::AttributeMap attrs_map, const platform::CPUPlace expected_place, const bool stop_gradient = false) { - pybind11::gil_scoped_release release; - return self.Trace(op, inputs, outputs, attrs_map, expected_place, - stop_gradient); - }) - .def("trace", - [](imperative::Tracer& self, imperative::OpBase* op, - const imperative::VarBasePtrMap& inputs, - imperative::VarBasePtrMap* outputs, - framework::AttributeMap attrs_map, - const platform::CUDAPlace expected_place, - const bool stop_gradient = false) { - pybind11::gil_scoped_release release; - return self.Trace(op, inputs, outputs, attrs_map, expected_place, - stop_gradient); + auto ins = ConvertToVarBasePtrMap(inputs); + auto outs = ConvertToVarBasePtrMap(outputs); + { + py::gil_scoped_release release; + self.Trace(op, std::move(ins), &outs, attrs_map, expected_place, + stop_gradient); + } }) - .def("py_trace", &imperative::Tracer::PyTrace, - pybind11::return_value_policy::take_ownership); + .def("trace", [](imperative::Tracer &self, imperative::OpBase *op, + const PyVarBaseMap &inputs, const PyVarBaseMap &outputs, + framework::AttributeMap attrs_map, + const platform::CUDAPlace expected_place, + const bool stop_gradient = false) { + auto ins = ConvertToVarBasePtrMap(inputs); + auto outs = ConvertToVarBasePtrMap(outputs); + { + py::gil_scoped_release release; + self.Trace(op, std::move(ins), &outs, attrs_map, expected_place, + stop_gradient); + } + }); // define parallel context - pybind11::class_ parallel_strategy( - *m, "ParallelStrategy", ""); - parallel_strategy.def(pybind11::init()) + py::class_ parallel_strategy( + m, "ParallelStrategy", ""); + parallel_strategy.def(py::init()) .def_property( "nranks", - [](const imperative::ParallelStrategy& self) { return self.nranks_; }, - [](imperative::ParallelStrategy& self, int nranks) { + [](const imperative::ParallelStrategy &self) { return self.nranks_; }, + [](imperative::ParallelStrategy &self, int nranks) { self.nranks_ = nranks; }) .def_property("local_rank", - [](const imperative::ParallelStrategy& self) { + [](const imperative::ParallelStrategy &self) { return self.local_rank_; }, - [](imperative::ParallelStrategy& self, int local_rank) { + [](imperative::ParallelStrategy &self, int local_rank) { self.local_rank_ = local_rank; }) .def_property( "trainer_endpoints", - [](const imperative::ParallelStrategy& self) { + [](const imperative::ParallelStrategy &self) { return self.trainer_endpoints_; }, - [](imperative::ParallelStrategy& self, std::vector eps) { + [](imperative::ParallelStrategy &self, std::vector eps) { self.trainer_endpoints_ = eps; }) .def_property("current_endpoint", - [](const imperative::ParallelStrategy& self) { + [](const imperative::ParallelStrategy &self) { return self.current_endpoint_; }, - [](imperative::ParallelStrategy& self, - const std::string& ep) { self.current_endpoint_ = ep; }); + [](imperative::ParallelStrategy &self, + const std::string &ep) { self.current_endpoint_ = ep; }); #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32) - pybind11::class_ nccl_ctx( - *m, "NCCLParallelContext"); + py::class_ nccl_ctx(m, + "NCCLParallelContext"); nccl_ctx - .def(pybind11::init()) - .def("init", [](imperative::NCCLParallelContext& self) { self.Init(); }); + .def(py::init()) + .def("init", [](imperative::NCCLParallelContext &self) { self.Init(); }); #endif } diff --git a/paddle/fluid/pybind/imperative.h b/paddle/fluid/pybind/imperative.h index f9d4a7c990e23b30eb7f5086fe56587f7c38bd22..cfe185bbfbbf327c8a2e75ed5be69dc5c680c05d 100644 --- a/paddle/fluid/pybind/imperative.h +++ b/paddle/fluid/pybind/imperative.h @@ -24,29 +24,6 @@ limitations under the License. */ namespace paddle { namespace pybind { -class Layer : public imperative::Layer { - public: - using imperative::Layer::Layer; // Inherit constructors - - std::vector Forward( - const std::vector& inputs) override { - PYBIND11_OVERLOAD(std::vector, Layer, Forward, - inputs); // NOLINT - } -}; - -class PYBIND11_HIDDEN PyOpBase : public imperative::OpBase { - public: - using imperative::OpBase::OpBase; // Inherit constructors - - PyOpBase(const std::string& name) : OpBase(name) {} -}; - -class PyVarBase : public imperative::VarBase { - public: - using imperative::VarBase::VarBase; // Inherit constructors -}; - void BindImperative(pybind11::module* m); } // namespace pybind diff --git a/paddle/fluid/pybind/inference_api.cc b/paddle/fluid/pybind/inference_api.cc index b650225c64a9a37e46d5b6f14eb2f03bebbaa71f..27f0e30d021534fd147e928b9eaf3c4ff040468e 100644 --- a/paddle/fluid/pybind/inference_api.cc +++ b/paddle/fluid/pybind/inference_api.cc @@ -17,7 +17,9 @@ #include #include #include +#include #include +#include #include #include "paddle/fluid/inference/api/analysis_predictor.h" #include "paddle/fluid/inference/api/paddle_inference_api.h" @@ -45,6 +47,10 @@ static void BindNativePredictor(py::module *m); static void BindAnalysisConfig(py::module *m); static void BindAnalysisPredictor(py::module *m); +#ifdef PADDLE_WITH_MKLDNN +static void BindMkldnnQuantizerConfig(py::module *m); +#endif + void BindInferenceApi(py::module *m) { BindPaddleDType(m); BindPaddleBuf(m); @@ -55,7 +61,9 @@ void BindInferenceApi(py::module *m) { BindNativePredictor(m); BindAnalysisConfig(m); BindAnalysisPredictor(m); - +#ifdef PADDLE_WITH_MKLDNN + BindMkldnnQuantizerConfig(m); +#endif m->def("create_paddle_predictor", &paddle::CreatePaddlePredictor); m->def("create_paddle_predictor", @@ -229,7 +237,7 @@ void BindAnalysisConfig(py::module *m) { py::arg("workspace_size") = 1 << 20, py::arg("max_batch_size") = 1, py::arg("min_subgraph_size") = 3, py::arg("precision_mode") = AnalysisConfig::Precision::kFloat32, - py::arg("use_static") = true) + py::arg("use_static") = true, py::arg("use_calib_mode") = false) .def("enable_anakin_engine", &AnalysisConfig::EnableAnakinEngine, py::arg("max_batch_size") = 1, py::arg("max_input_shape") = @@ -249,6 +257,11 @@ void BindAnalysisConfig(py::module *m) { .def("cpu_math_library_num_threads", &AnalysisConfig::cpu_math_library_num_threads) .def("to_native_config", &AnalysisConfig::ToNativeConfig) + .def("enable_quantizer", &AnalysisConfig::EnableMkldnnQuantizer) +#ifdef PADDLE_WITH_MKLDNN + .def("quantizer_config", &AnalysisConfig::mkldnn_quantizer_config, + py::return_value_policy::reference) +#endif .def("set_mkldnn_op", &AnalysisConfig::SetMKLDNNOp) .def("set_model_buffer", &AnalysisConfig::SetModelBuffer) .def("model_from_memory", &AnalysisConfig::model_from_memory) @@ -256,6 +269,28 @@ void BindAnalysisConfig(py::module *m) { py::return_value_policy::reference); } +#ifdef PADDLE_WITH_MKLDNN +void BindMkldnnQuantizerConfig(py::module *m) { + py::class_ quantizer_config(*m, + "MkldnnQuantizerConfig"); + quantizer_config.def(py::init()) + .def(py::init<>()) + .def("set_quant_data", + [](MkldnnQuantizerConfig &self, + const std::vector &data) { + auto warmup_data = + std::make_shared>(data); + self.SetWarmupData(warmup_data); + return; + }) + .def("set_quant_batch_size", &MkldnnQuantizerConfig::SetWarmupBatchSize) + .def( + "set_enabled_op_types", + (void (MkldnnQuantizerConfig::*)(std::unordered_set &)) & + MkldnnQuantizerConfig::SetEnabledOpTypes); +} +#endif + void BindAnalysisPredictor(py::module *m) { py::class_(*m, "AnalysisPredictor") .def(py::init()) @@ -272,7 +307,9 @@ void BindAnalysisPredictor(py::module *m) { .def("zero_copy_run", &AnalysisPredictor::ZeroCopyRun) .def("clone", &AnalysisPredictor::Clone) .def("scope", &AnalysisPredictor::scope, - py::return_value_policy::reference); + py::return_value_policy::reference) + .def("SaveOptimModel", &AnalysisPredictor::SaveOptimModel, + py::arg("dir")); } } // namespace pybind diff --git a/paddle/fluid/pybind/ir.cc b/paddle/fluid/pybind/ir.cc index 798e488f5b0c55c9eabdc420baa7bb0380b2fdba..abc10765e4a37000412534e5396b7e9ef792a00d 100644 --- a/paddle/fluid/pybind/ir.cc +++ b/paddle/fluid/pybind/ir.cc @@ -24,6 +24,7 @@ #include "paddle/fluid/framework/ir/graph_pattern_detector.h" #include "paddle/fluid/framework/ir/node.h" #include "paddle/fluid/framework/op_desc.h" +#include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/var_desc.h" #include "pybind11/stl.h" @@ -37,6 +38,7 @@ using paddle::framework::ir::TopologySortOperations; using paddle::framework::ir::BuildOperationAdjList; using paddle::framework::OpDesc; using paddle::framework::ProgramDesc; +using paddle::framework::Scope; using paddle::framework::VarDesc; using pybind11::return_value_policy; @@ -57,12 +59,15 @@ void BindGraph(py::module *m) { .def(py::init()) .def("clone", &Graph::Clone) .def("has", &Graph::Has) + .def("get_bool", &Graph::Get) .def("get_int", &Graph::Get) .def("get_float", &Graph::Get) .def("get_double", &Graph::Get) .def("get_string", &Graph::Get) .def("get_marked_nodes", &Graph::Get>, return_value_policy::reference) + .def("set", [](Graph &self, const std::string &attr_name, + bool attr) { return self.Set(attr_name, new bool(attr)); }) .def("set", [](Graph &self, const std::string &attr_name, int attr) { return self.Set(attr_name, new int(attr)); }) .def("set", @@ -90,6 +95,10 @@ void BindGraph(py::module *m) { return self.Set(attr_name, new std::unordered_set(attr)); }) + .def("set_not_owned", + [](Graph &self, const std::string &attr_name, Scope &attr) { + self.SetNotOwned(attr_name, &attr); + }) .def("erase", &Graph::Erase) .def("nodes", &Graph::Nodes, return_value_policy::reference) .def("create_var_node", diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc index 63d37223ca7a83ba47081a6b3fc90ec510866cf8..f6096fb8ca43b6cac3f1bc03de377c375a1c222d 100644 --- a/paddle/fluid/pybind/pybind.cc +++ b/paddle/fluid/pybind/pybind.cc @@ -18,6 +18,7 @@ limitations under the License. */ #include // NOLINT // for call_once #include #include +#include #include #include @@ -38,13 +39,12 @@ limitations under the License. */ #include "paddle/fluid/framework/scope_pool.h" #include "paddle/fluid/framework/selected_rows.h" #include "paddle/fluid/framework/version.h" -#include "paddle/fluid/imperative/layer.h" -#include "paddle/fluid/imperative/profiler.h" #include "paddle/fluid/memory/allocation/allocator_strategy.h" #include "paddle/fluid/memory/allocation/legacy_allocator.h" #include "paddle/fluid/operators/activation_op.h" #include "paddle/fluid/operators/py_func_op.h" #include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h" +#include "paddle/fluid/platform/cpu_helper.h" #include "paddle/fluid/platform/cpu_info.h" #include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/init.h" @@ -58,6 +58,7 @@ limitations under the License. */ #include "paddle/fluid/pybind/imperative.h" #include "paddle/fluid/pybind/inference_api.h" #include "paddle/fluid/pybind/ir.h" + #ifndef _WIN32 #include "paddle/fluid/pybind/nccl_wrapper_py.h" #endif @@ -76,6 +77,10 @@ limitations under the License. */ #include "paddle/fluid/platform/gpu_info.h" #endif +#ifdef PADDLE_WITH_DISTRIBUTE +#include "paddle/fluid/pybind/communicator_py.h" +#endif + #include "pybind11/stl.h" DEFINE_bool(reader_queue_speed_test_mode, false, @@ -141,7 +146,12 @@ static inline int PlaceIndex(const PlaceType &p) { return static_cast(paddle::platform::Place(p).which()); } -PYBIND11_MODULE(core, m) { +#ifdef PADDLE_WITH_AVX +PYBIND11_MODULE(core_avx, m) { +#else +PYBIND11_MODULE(core_noavx, m) { +#endif + // Not used, just make sure cpu_info.cc is linked. paddle::platform::CpuTotalPhysicalMemory(); @@ -155,6 +165,8 @@ PYBIND11_MODULE(core, m) { BindException(&m); + m.def("set_num_threads", &platform::SetNumThreads); + m.def( "_append_python_callable_object_and_return_id", [](py::object py_obj) -> size_t { @@ -184,121 +196,6 @@ PYBIND11_MODULE(core, m) { m.def("print_mem_usage", []() { return memory::allocation::GPUMemMonitor.PrintMemUsage(); }); - m.def("start_imperative_gperf_profiler", - []() { imperative::StartProfile(); }); - - m.def("stop_imperative_gperf_profiler", []() { imperative::StopProfile(); }); - - py::class_(m, "VarBase", R"DOC()DOC") - .def( - py::init, const paddle::platform::CPUPlace, - bool, bool>()) - .def( - py::init, - const paddle::platform::CUDAPlace, bool, bool>()) - .def("_run_backward", - [](imperative::VarBase &self) { self.RunBackward(); }) - .def("_grad_name", &imperative::VarBase::GradName) - .def("_grad_value", &imperative::VarBase::GradValue) - .def("_clear_gradient", &imperative::VarBase::ClearGradient) - .def("_grad_ivar", - [](const imperative::VarBase &self) { return self.grads_; }, - py::return_value_policy::reference) - .def("_copy_to", - [](const imperative::VarBase &self, const platform::CPUPlace &place, - bool blocking) { - std::unique_ptr new_var = - self.NewVarBase(place, blocking); - return new_var.release(); - }, - py::return_value_policy::take_ownership) - .def("_copy_to", - [](const imperative::VarBase &self, const platform::CUDAPlace &place, - bool blocking) { - std::unique_ptr new_var = - self.NewVarBase(place, blocking); - return new_var.release(); - }, - py::return_value_policy::take_ownership) - .def("value", [](const imperative::VarBase &self) { return self.var_; }, - py::return_value_policy::reference) - .def_property("name", &imperative::VarBase::Name, - &imperative::VarBase::SetName) - .def_property_readonly("shape", &imperative::VarBase::Shape) - .def_property_readonly("dtype", &imperative::VarBase::DataType) - .def_property("persistable", &imperative::VarBase::IsPersistable, - &imperative::VarBase::SetPersistable) - .def_property("stop_gradient", &imperative::VarBase::IsStopGradient, - &imperative::VarBase::SetStopGradient); - - py::class_(m, "OpBase", R"DOC()DOC") - .def(py::init()) - .def("register_backward_hooks", - [](imperative::OpBase &self, const py::object &callable, - bool front = false) { - self.RegisterBackwardHooks(callable, front); - }, - py::arg("callable"), py::arg("front") = false) - .def_property("_trace_id", - [](const imperative::OpBase &self) { - pybind11::gil_scoped_release release; - return self.trace_id_; - }, - [](imperative::OpBase &self, int trace_id) { - pybind11::gil_scoped_release release; - self.trace_id_ = trace_id; - }, - py::return_value_policy::reference) - .def_property( - "forward_id", - [](const imperative::OpBase &self) { return self.forward_id_; }, - [](imperative::OpBase &self, int forward_id) { - self.forward_id_ = forward_id; - }, - py::return_value_policy::reference) - .def_property_readonly("type", &imperative::OpBase::Type) - .def_property( - "backward_id", - [](const imperative::OpBase &self) { return self.backward_id_; }, - [](imperative::OpBase &self, int backward_id) { - self.backward_id_ = backward_id; - }, - py::return_value_policy::reference); - - py::class_ layer(m, "Layer"); - layer.def(py::init<>()) - .def("forward", [](imperative::Layer &self, - const std::vector &inputs) { - return self.Forward(inputs); - }); - - py::class_(m, "PyLayer") - .def(py::init<>()) - .def_static( - "apply", - [](int func_id, const std::vector &inputs) - -> std::vector { - auto ret_vars = imperative::PyLayer::Apply(func_id, inputs); - std::vector outputs; - outputs.reserve(ret_vars.size()); - for (size_t i = 0U; i != ret_vars.size(); ++i) { - framework::Variable *v = ret_vars[i]; - // TODO(minqiyang): use unique_name generator to set a name - outputs.emplace_back( - new imperative::VarBase("", v, nullptr, true)); - } - - return outputs; - }, - py::return_value_policy::take_ownership) - .def_static("register_func", - [](int func_id, const py::object &callable) { - imperative::PyLayer::RegisterFunc(func_id, callable); - }) - .def_static("num_funcs", &imperative::PyLayer::NumFuncs); - BindImperative(&m); py::class_(m, "Tensor", py::buffer_protocol()) @@ -339,6 +236,7 @@ PYBIND11_MODULE(core, m) { [](Tensor &self, paddle::platform::CUDAPinnedPlace &place) { self.mutable_data(place); }) + .def("_clear", &Tensor::clear) .def("set", PyCPUTensorSetFromArray) .def("set", PyCPUTensorSetFromArray) .def("set", PyCPUTensorSetFromArray) @@ -372,7 +270,12 @@ PYBIND11_MODULE(core, m) { .def("_get_double_element", TensorGetElement) .def("_place", [](Tensor &self) { return self.place(); }) .def("_dtype", [](Tensor &self) { return self.type(); }) - .def("__getitem__", PySliceTensor, py::return_value_policy::reference); + .def("__getitem__", PySliceTensor, py::return_value_policy::reference) + .def("__str__", [](const Tensor &self) { + std::stringstream ostr; + ostr << self; + return ostr.str(); + }); py::class_(m, "LoDTensor", R"DOC( LoDTensor is a Tensor with optional LoD information. @@ -383,8 +286,8 @@ PYBIND11_MODULE(core, m) { LoD is short for Level of Details and is usually used for varied sequence length. You can skip the following comment if you don't need optional LoD. - For example, a LoDTensor X can look like the example below. It contains - 2 sequences. The first has length 2 and the second has length 3, as + For example, a LoDTensor X can look like the example below. It contains + 2 sequences. The first has length 2 and the second has length 3, as described by x.lod. The first tensor dimension 5=2+3 is calculated from LoD if it's available. @@ -392,7 +295,7 @@ PYBIND11_MODULE(core, m) { columns, hence [5, 2]. x.lod = [[2, 3]] - + x.data = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]] x.shape = [5, 2] @@ -591,7 +494,12 @@ PYBIND11_MODULE(core, m) { Returns: out (Tensor): new Tensor(NOT LoDTensor). - )DOC"); + )DOC") + .def("__str__", [](const LoDTensor &self) { + std::stringstream ostr; + ostr << self; + return ostr.str(); + }); py::class_(m, "SelectedRows") .def("__init__", @@ -1013,10 +921,38 @@ All parameter, weight, gradient are variables in Paddle. [](const OperatorBase &op) { return op.OutputVars(false); }) .def("support_gpu", &OperatorBase::SupportGPU); + py::class_(m, "ExecutorPrepareContext") + .def(py::init()); + py::class_(m, "Executor") .def(py::init()) .def("close", &Executor::Close) - .def("run_from_dataset", &Executor::RunFromDataset) + .def("run_from_dataset", &Executor::RunFromDataset, + py::call_guard()) + .def("run_prepared_ctx", + [](Executor &self, ExecutorPrepareContext *ctx, Scope *scope, + std::map *feed_targets, + std::map *fetch_targets, + bool create_local_scope = true, bool create_vars = true, + const std::string &feed_holder_name = "feed", + const std::string &fetch_holder_name = "fetch") { + pybind11::gil_scoped_release release; + self.RunPreparedContext(ctx, scope, feed_targets, fetch_targets, + create_local_scope, create_vars, + feed_holder_name, fetch_holder_name); + }) + .def("run_cached_prepared_ctx", + [](Executor &self, ExecutorPrepareContext *ctx, Scope *scope, + bool create_local_scope = true, bool create_vars = true, + bool keep_kids = false) { + pybind11::gil_scoped_release release; + self.RunPreparedContext(ctx, scope, create_local_scope, + create_vars, keep_kids); + }) + .def("prepare_ctx_cache", &Executor::PrepareCtxCache, + py::call_guard()) + .def("create_variables", &Executor::CreateVariables, + py::call_guard()) .def("run", [](Executor &self, const ProgramDesc &prog, Scope *scope, int block_id, bool create_local_scope, bool create_vars, const std::vector &fetch_vars) { @@ -1069,7 +1005,7 @@ All parameter, weight, gradient are variables in Paddle. Examples: .. code-block:: python - + import paddle.fluid as fluid arr = fluid.LoDTensorArray() @@ -1191,15 +1127,23 @@ All parameter, weight, gradient are variables in Paddle. Examples: .. code-block:: python + x = fluid.layers.data(name='x', shape=[13], dtype='float32') + y = fluid.layers.data(name='y', shape=[1], dtype='float32') + y_predict = fluid.layers.fc(input=x, size=1, act=None) + + cost = fluid.layers.square_error_cost(input=y_predict, label=y) + avg_loss = fluid.layers.mean(cost) + + sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) + sgd_optimizer.minimize(avg_loss) + exec_strategy = fluid.ExecutionStrategy() exec_strategy.num_threads = 4 - train_exe = fluid.ParallelExecutor(use_cuda=True, - loss_name=loss.name, + train_exe = fluid.ParallelExecutor(use_cuda=False, + loss_name=avg_loss.name, exec_strategy=exec_strategy) - train_loss, = train_exe.run([loss.name], feed=feed_dict) - )DOC"); exec_strategy.def(py::init()) @@ -1235,7 +1179,8 @@ All parameter, weight, gradient are variables in Paddle. }, R"DOC(The type is BOOL, allow_op_delay represents whether to delay the communication operators to run, it may make the execution faster. - Note that in some models, allow_op_delay may cause program hang. Default False.)DOC") + Note that this option is invalid now, and it will be removed in + next version. Default False.)DOC") .def_property( "num_iteration_per_drop_scope", [](const ExecutionStrategy &self) { @@ -1247,7 +1192,8 @@ All parameter, weight, gradient are variables in Paddle. R"DOC(The type is INT, num_iteration_per_drop_scope indicates how many iterations to clean up the temp variables which is generated during execution. It may make the execution faster, - because the temp variable's shape maybe the same between two iterations. Default 100. + because the temp variable's shape maybe the same between two iterations. + Default 1. NOTES: 1. If you fetch data when calling the 'run', the ParallelExecutor @@ -1289,14 +1235,9 @@ All parameter, weight, gradient are variables in Paddle. Examples: .. code-block:: python - build_strategy = fluid.BuildStrategy() - build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce - - train_exe = fluid.ParallelExecutor(use_cuda=True, - loss_name=loss.name, - build_strategy=build_strategy) - - train_loss, = train_exe.run([loss.name], feed=feed_dict) + import paddle.fluid as fluid + build_strategy = fluid.BuildStrategy() + build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce )DOC"); py::enum_(build_strategy, "ReduceStrategy") @@ -1318,11 +1259,19 @@ All parameter, weight, gradient are variables in Paddle. self.reduce_ = strategy; }, R"DOC(The type is STR, there are two reduce strategies in ParallelExecutor, - 'AllReduce' and 'Reduce'. If you want that all the parameters' - optimization are done on all devices independently, you should choose 'AllReduce'; - if you choose 'Reduce', all the parameters' optimization will be evenly distributed - to different devices, and then broadcast the optimized parameter to other devices. - In some models, `Reduce` is faster. Default 'AllReduce'. )DOC") + 'AllReduce' and 'Reduce'. If you want that all the parameters' + optimization are done on all devices independently, you should choose 'AllReduce'; + if you choose 'Reduce', all the parameters' optimization will be evenly distributed + to different devices, and then broadcast the optimized parameter to other devices. + In some models, `Reduce` is faster. Default 'AllReduce'. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + build_strategy = fluid.BuildStrategy() + build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce + )DOC") .def_property( "gradient_scale_strategy", [](const BuildStrategy &self) { return self.gradient_scale_; }, @@ -1332,10 +1281,18 @@ All parameter, weight, gradient are variables in Paddle. self.gradient_scale_ = strategy; }, R"DOC(The type is STR, there are three ways of defining :math:`loss@grad` in - ParallelExecutor, 'CoeffNumDevice', 'One' and 'Customized'. By default, - ParallelExecutor sets the :math:`loss@grad` according to the number of devices. - If you want to customize :math:`loss@grad`, you can choose 'Customized'. - Default 'CoeffNumDevice'.)DOC") + ParallelExecutor, 'CoeffNumDevice', 'One' and 'Customized'. By default, + ParallelExecutor sets the :math:`loss@grad` according to the number of devices. + If you want to customize :math:`loss@grad`, you can choose 'Customized'. + Default 'CoeffNumDevice'. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + build_strategy = fluid.BuildStrategy() + build_strategy.gradient_scale_strategy = True + )DOC") .def_property( "debug_graphviz_path", [](const BuildStrategy &self) { return self.debug_graphviz_path_; }, @@ -1344,8 +1301,16 @@ All parameter, weight, gradient are variables in Paddle. self.debug_graphviz_path_ = path; }, R"DOC(The type is STR, debug_graphviz_path indicate the path that - writing the SSA Graph to file in the form of graphviz, you. - It is useful for debugging. Default "")DOC") + writing the SSA Graph to file in the form of graphviz. + It is useful for debugging. Default "" + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + build_strategy = fluid.BuildStrategy() + build_strategy.debug_graphviz_path = "" + )DOC") .def_property( "enable_sequential_execution", [](const BuildStrategy &self) { @@ -1355,7 +1320,15 @@ All parameter, weight, gradient are variables in Paddle. PADDLE_ENFORCE(!self.IsFinalized(), "BuildStrategy is finlaized."); self.enable_sequential_execution_ = b; }, - R"DOC(The type is BOOL. If set True, the execution order of ops would be the same as what is in the program. Default False.)DOC") + R"DOC(The type is BOOL. If set True, the execution order of ops would be the same as what is in the program. Default False. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + build_strategy = fluid.BuildStrategy() + build_strategy.enable_sequential_execution = True + )DOC") .def_property( "remove_unnecessary_lock", [](const BuildStrategy &self) { @@ -1365,11 +1338,22 @@ All parameter, weight, gradient are variables in Paddle. PADDLE_ENFORCE(!self.IsFinalized(), "BuildStrategy is finlaized."); self.remove_unnecessary_lock_ = b; }, - R"DOC(The type is BOOL. If set True, some locks in GPU ops would be released and ParallelExecutor would run faster. Default True.)DOC") + R"DOC(The type is BOOL. If set True, some locks in GPU ops would be released and ParallelExecutor would run faster. Default True. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + build_strategy = fluid.BuildStrategy() + build_strategy.remove_unnecessary_lock = True + )DOC") .def_property( "num_trainers", [](const BuildStrategy &self) { return self.num_trainers_; }, [](BuildStrategy &self, int num_trainers) { +#ifdef WIN32 + PADDLE_THROW("Windows has NO support to distribute mode."); +#endif self.num_trainers_ = num_trainers; }) .def_property( @@ -1384,6 +1368,34 @@ All parameter, weight, gradient are variables in Paddle. [](BuildStrategy &self, int trainer_id) { self.trainer_id_ = trainer_id; }) + .def_property( + "nccl_comm_num", + [](const BuildStrategy &self) { return self.nccl_comm_num_; }, + [](BuildStrategy &self, int nccl_comm_num) { + self.nccl_comm_num_ = nccl_comm_num; + }) + .def_property("use_hierarchical_allreduce_", + [](const BuildStrategy &self) { + return self.use_hierarchical_allreduce_; + }, + [](BuildStrategy &self, bool use) { + self.use_hierarchical_allreduce_ = use; + }) + .def_property("hierarchical_allreduce_inter_nranks_", + [](const BuildStrategy &self) { + return self.hierarchical_allreduce_inter_nranks_; + }, + [](BuildStrategy &self, int nranks) { + self.hierarchical_allreduce_inter_nranks_ = nranks; + }) + .def_property("hierarchical_allreduce_exter_nranks_", + [](const BuildStrategy &self) { + return self.hierarchical_allreduce_exter_nranks_; + }, + [](BuildStrategy &self, int nranks) { + self.hierarchical_allreduce_exter_nranks_ = nranks; + }) + .def_property( "fuse_elewise_add_act_ops", [](const BuildStrategy &self) { @@ -1394,8 +1406,16 @@ All parameter, weight, gradient are variables in Paddle. self.fuse_elewise_add_act_ops_ = b; }, R"DOC(The type is BOOL, fuse_elewise_add_act_ops indicate whether - to fuse elementwise_add_op and activation_op, - it may make the execution faster. Default False)DOC") + to fuse elementwise_add_op and activation_op, + it may make the execution faster. Default False + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + build_strategy = fluid.BuildStrategy() + build_strategy.fuse_elewise_add_act_ops = True + )DOC") .def_property( "fuse_relu_depthwise_conv", [](const BuildStrategy &self) { @@ -1406,10 +1426,18 @@ All parameter, weight, gradient are variables in Paddle. self.fuse_relu_depthwise_conv_ = b; }, R"DOC(The type is BOOL, fuse_relu_depthwise_conv indicate whether - to fuse relu and depthwise_conv2d, - it will save GPU memory and may make the execution faster. - This options is only available in GPU devices. - Default False.)DOC") + to fuse relu and depthwise_conv2d, + it will save GPU memory and may make the execution faster. + This options is only available in GPU devices. + Default False. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + build_strategy = fluid.BuildStrategy() + build_strategy.fuse_relu_depthwise_conv = True + )DOC") .def_property( "fuse_broadcast_ops", [](const BuildStrategy &self) { return self.fuse_broadcast_ops_; }, @@ -1446,24 +1474,40 @@ All parameter, weight, gradient are variables in Paddle. Current implementation doesn't support FP16 training and CPU. And only synchronous on one machine, not all machines. - Default False)DOC") + Default False + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + build_strategy = fluid.BuildStrategy() + build_strategy.sync_batch_norm = True + )DOC") .def_property( "memory_optimize", [](const BuildStrategy &self) { return self.memory_optimize_; }, [](BuildStrategy &self, bool b) { self.memory_optimize_ = b; }, - R"DOC(The type is BOOL, memory opitimize aims to save total memory + R"DOC(The type is BOOL, memory opitimize aims to save total memory consumption, set to True to enable it. - - Memory Optimize is our experimental feature, some variables + + Memory Optimize is our experimental feature, some variables may be reused/removed by optimize strategy. If you need to fetch some variable values when using this feature, please set the persistable property of the variables to True. - + Default False)DOC") .def_property( "is_distribution", [](const BuildStrategy &self) { return self.is_distribution_; }, - [](BuildStrategy &self, bool b) { self.is_distribution_ = b; }) + [](BuildStrategy &self, bool b) { +#ifdef WIN32 + if (b) { + PADDLE_THROW("Windows has NO support to distribute mode."); + } +#else + self.is_distribution_ = b; +#endif + }) .def_property("async_mode", [](const BuildStrategy &self) { return self.async_mode_; }, [](BuildStrategy &self, bool b) { self.async_mode_ = b; }) @@ -1475,14 +1519,26 @@ All parameter, weight, gradient are variables in Paddle. "fuse_all_reduce_ops", [](const BuildStrategy &self) { return self.fuse_all_reduce_ops_; }, [](BuildStrategy &self, bool b) { self.fuse_all_reduce_ops_ = b; }) + .def_property("enable_backward_optimizer_op_deps", + [](const BuildStrategy &self) { + return self.enable_backward_optimizer_op_deps_; + }, + [](BuildStrategy &self, bool b) { + self.enable_backward_optimizer_op_deps_ = b; + }) .def_property( "cache_runtime_context", [](const BuildStrategy &self) { return self.cache_runtime_context_; }, [](BuildStrategy &self, bool b) { self.cache_runtime_context_ = b; }) .def_property( - "cache_expected_kernel", - [](const BuildStrategy &self) { return self.cache_expected_kernel_; }, - [](BuildStrategy &self, bool b) { self.cache_expected_kernel_ = b; }) + "mkldnn_enabled_op_types", + [](const BuildStrategy &self) { + return self.mkldnn_enabled_op_types_; + }, + [](BuildStrategy &self, + const std::unordered_set &mkldnn_enabled_op_types) { + self.mkldnn_enabled_op_types_ = mkldnn_enabled_op_types; + }) .def("_finalize_strategy_and_create_passes", [](BuildStrategy &self) -> std::shared_ptr { return self.CreatePassesFromStrategy(true); @@ -1528,6 +1584,9 @@ All parameter, weight, gradient are variables in Paddle. BindNode(&m); BindInferenceApi(&m); BindDataset(&m); +#ifdef PADDLE_WITH_DISTRIBUTE + BindCommunicator(&m); +#endif } } // namespace pybind } // namespace paddle diff --git a/paddle/fluid/pybind/reader_py.cc b/paddle/fluid/pybind/reader_py.cc index af7d30552ed47c0fbe26090b328cc7128b90f84d..4c304e8626b61c3fb0637e30722f86e43f52d707 100644 --- a/paddle/fluid/pybind/reader_py.cc +++ b/paddle/fluid/pybind/reader_py.cc @@ -31,6 +31,7 @@ class MultiDeviceFeedReader { public: using ResultDictList = std::vector>; + using ResultList = std::vector>; MultiDeviceFeedReader( const std::shared_ptr &queue, @@ -81,6 +82,21 @@ class MultiDeviceFeedReader { return result; } + ResultList ReadNextList() { + bool success = WaitFutures(); + if (!success) { + return {}; + } + + ResultList result; + result.reserve(ret_.size()); + for (size_t i = 0; i < ret_.size(); ++i) { + result.emplace_back(std::move(ret_[i])); + } + ReadAsync(); + return result; + } + void Reset() { Shutdown(); Start(); @@ -142,6 +158,8 @@ void BindReader(py::module *module) { py::class_(m, "MultiDeviceFeedReader", "") .def("read_next", &MultiDeviceFeedReader::ReadNext, py::call_guard()) + .def("read_next_list", &MultiDeviceFeedReader::ReadNextList, + py::call_guard()) .def("reset", &MultiDeviceFeedReader::Reset, py::call_guard()); diff --git a/paddle/fluid/string/printf.h b/paddle/fluid/string/printf.h index 16bb3771f2e9bcc07028ef2039fed8691f9aab97..66b768665b6d0b97b4ca1470020132bfc9576bbb 100644 --- a/paddle/fluid/string/printf.h +++ b/paddle/fluid/string/printf.h @@ -105,14 +105,12 @@ void Printf(const char* fmt, const Args&... args) { Fprintf(std::cout, fmt, args...); } -template -std::string HumanReadableSize(T size) { +inline std::string HumanReadableSize(double f_size) { size_t i = 0; - double f_size = static_cast(size); double orig = f_size; const std::vector units( {"B", "kB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB"}); - while (f_size > 1024) { + while (f_size >= 1024) { f_size /= 1024; i++; } diff --git a/paddle/scripts/README.md b/paddle/scripts/README.md index 1db262f06d97665ee09b8e1d3485982b6b1b33d6..39db5a601d3d46c106a574870f02434bd4bd5cd1 100644 --- a/paddle/scripts/README.md +++ b/paddle/scripts/README.md @@ -108,6 +108,14 @@ RUN pip install /paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl && rm -f /*.wh Then build the image by running `docker build -t [REPO]/paddle:[TAG] .` under the directory containing your own `Dockerfile`. +We also release a script and Dockerfile for building PaddlePaddle docker images +across different cuda versions. To build these docker images, run: + +```bash +bash ./build_docker_images.sh +docker build -t [REPO]/paddle:tag -f [generated_docker_file] . +``` + - NOTE: note that you can choose different base images for your environment, you can find all the versions [here](https://hub.docker.com/r/nvidia/cuda/). ### Use Docker Images diff --git a/paddle/scripts/paddle_build.sh b/paddle/scripts/paddle_build.sh index f1630e0b509f214321ff8c3bb9857803be81ec16..e5e1ef6c25ecc74b3b1dce190ab2021b471263e2 100755 --- a/paddle/scripts/paddle_build.sh +++ b/paddle/scripts/paddle_build.sh @@ -52,10 +52,7 @@ function init() { fi } -function cmake_gen() { - mkdir -p ${PADDLE_ROOT}/build - cd ${PADDLE_ROOT}/build - +function cmake_base() { # build script will not fail if *.deb does not exist rm *.deb 2>/dev/null || true # delete previous built whl packages @@ -227,6 +224,7 @@ EOF -DWITH_MKL=${WITH_MKL:-ON} \ -DWITH_NGRAPH=${WITH_NGRAPH:-OFF} \ -DWITH_AVX=${WITH_AVX:-OFF} \ + -DNOAVX_CORE_FILE=${NOAVX_CORE_FILE:-""} \ -DWITH_GOLANG=${WITH_GOLANG:-OFF} \ -DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All} \ -DCUDA_ARCH_BIN=${CUDA_ARCH_BIN} \ @@ -249,6 +247,12 @@ EOF } +function cmake_gen() { + mkdir -p ${PADDLE_ROOT}/build + cd ${PADDLE_ROOT}/build + cmake_base $1 +} + function abort(){ echo "Your change doesn't follow PaddlePaddle's code style." 1>&2 echo "Please use pre-commit to check what is wrong." 1>&2 @@ -287,6 +291,17 @@ function check_style() { # Build #================================================= +function build_base() { + parallel_number=`nproc` + if [[ "$1" != "" ]]; then + parallel_number=$1 + fi + make clean + make -j ${parallel_number} + make install -j `nproc` +} + + function build() { mkdir -p ${PADDLE_ROOT}/build cd ${PADDLE_ROOT}/build @@ -295,13 +310,7 @@ function build() { Building in /paddle/build ... ============================================ EOF - parallel_number=`nproc` - if [[ "$1" != "" ]]; then - parallel_number=$1 - fi - make clean - make -j ${parallel_number} - make install -j `nproc` + build_base $1 } function build_mac() { @@ -334,6 +343,25 @@ EOF fi } + +function combine_avx_noavx_build() { + mkdir -p ${PADDLE_ROOT}/build.noavx + cd ${PADDLE_ROOT}/build.noavx + WITH_AVX=OFF + cmake_base ${PYTHON_ABI:-""} + build_base + + # build combined one + mkdir -p ${PADDLE_ROOT}/build + cd ${PADDLE_ROOT}/build + NOAVX_CORE_FILE=`find ${PADDLE_ROOT}/build.noavx/python/paddle/fluid/ -name "core_noavx.*"` + WITH_AVX=ON + + cmake_base ${PYTHON_ABI:-""} + build_base +} + + function run_brpc_test() { mkdir -p ${PADDLE_ROOT}/build cd ${PADDLE_ROOT}/build @@ -390,6 +418,19 @@ EOF ctest --output-on-failure -j $2 # make install should also be test when unittest make install -j 8 + + set +ex + if [ "$1" == "cp27-cp27m" ]; then + pip uninstall -y paddlepaddle + elif [ "$1" == "cp35-cp35m" ]; then + pip3.5 uninstall -y paddlepaddle + elif [ "$1" == "cp36-cp36m" ]; then + pip3.6 uninstall -y paddlepaddle + elif [ "$1" == "cp37-cp37m" ]; then + pip3.7 uninstall -y paddlepaddle + fi + set -ex + if [ "$1" == "cp27-cp27m" ]; then set -e pip install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl @@ -403,16 +444,6 @@ EOF fi paddle version - - if [ "$1" == "cp27-cp27m" ]; then - pip uninstall -y paddlepaddle - elif [ "$1" == "cp35-cp35m" ]; then - pip3.5 uninstall -y paddlepaddle - elif [ "$1" == "cp36-cp36m" ]; then - pip3.6 uninstall -y paddlepaddle - elif [ "$1" == "cp37-cp37m" ]; then - pip3.7 uninstall -y paddlepaddle - fi fi } @@ -465,6 +496,7 @@ function assert_api_spec_approvals() { "paddle/fluid/framework/ir/node.h" "paddle/fluid/framework/ir/graph.h" "paddle/fluid/framework/framework.proto" + "python/requirements.txt" "python/paddle/fluid/compiler.py" "python/paddle/fluid/__init__.py" "paddle/fluid/operators/distributed/send_recv.proto.in") @@ -473,34 +505,34 @@ function assert_api_spec_approvals() { echo "checking ${API_FILE} change, PR: ${GIT_PR_ID}, changes: ${API_CHANGE}" if [ "${API_CHANGE}" ] && [ "${GIT_PR_ID}" != "" ]; then # NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable. - # approval_user_list: velconia 1979255,XiaoguangHu01 46782768,chengduoZH 30176695,Xreki 12538138,luotao1 6836917,sneaxiy 32832641,tensor-tang 21351065,jacquesqiao 3048612,typhoonzero 13348433,shanyi15 35982308. + # approval_user_list: XiaoguangHu01 46782768,chengduoZH 30176695,Xreki 12538138,luotao1 6836917,sneaxiy 32832641,tensor-tang 21351065,jacquesqiao 3048612,xsrobin 50069408,qingqing01 7845005,junjun315 3124479. + approval_line=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000` if [ "${API_FILE}" == "paddle/fluid/API.spec" ];then - APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \ - python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 35982308 46782768 30176695` + APPROVALS=`echo ${approval_line}|python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 50069408 46782768 30176695 6836917 7845005` if [ "${APPROVALS}" == "TRUE" ];then - APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \ - python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 35982308` + APPROVALS=`echo ${approval_line}|python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 50069408` fi elif [ "${API_FILE}" == "CMakeLists.txt" ];then - APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \ - python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 6836917 46782768 30176695` + APPROVALS=`echo ${approval_line}|python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 6836917 46782768 30176695` elif [ "${API_FILE}" == "python/paddle/fluid/__init__.py" ];then - APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \ - python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 35982308` + APPROVALS=`echo ${approval_line}|python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 50069408` + elif [ "${API_FILE}" == "python/requirements.txt" ];then + APPROVALS=`echo ${approval_line}|python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 3124479 6836917` else - APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \ - python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641` + APPROVALS=`echo ${approval_line}|python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 21351065 3048612 46782768 30176695 12538138 6836917 32832641` fi echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}" if [ "${APPROVALS}" == "FALSE" ]; then if [ "${API_FILE}" == "paddle/fluid/API.spec" ];then - echo "You must have one RD (chengduoZH or XiaoguangHu01) and one PM (shanyi15) approval for the api change! ${API_FILE} for the management reason of API interface and API document." + echo "You must have one RD (chengduoZH or XiaoguangHu01 or qingqing01 or luotao1) and one PM (xsrobin) approval for the api change! ${API_FILE} for the management reason of API interface and API document." elif [ "${API_FILE}" == "CMakeLists.txt" ];then echo "You must have one RD (luotao1 or chengduoZH or XiaoguangHu01) approval for the cmakelist change! ${API_FILE} for the management reason of the Compilation parameter." + elif [ "${API_FILE}" == "python/requirements.txt" ];then + echo "You must have one RD (junjun315 or luotao1) approval for the python/requirements.txt change! ${API_FILE} for the management reason of the Compilation parameter." elif [ "${API_FILE}" == "python/paddle/fluid/__init__.py" ];then - echo "You must have shanyi15 approval for the python/paddle/fluid/__init__.py change! ${API_FILE} for the management reason of the environment variables." + echo "You must have xsrobin approval for the python/paddle/fluid/__init__.py change! ${API_FILE} for the management reason of the environment variables." else - echo "You must have one RD (velconia,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE} for the management reason of the underlying code for fluid." + echo "You must have one RD (XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao) approval for the api change! ${API_FILE} for the management reason of the underlying code for fluid." fi exit 1 fi @@ -510,10 +542,10 @@ function assert_api_spec_approvals() { HAS_CONST_CAST=`git diff -U0 upstream/$BRANCH |grep -o -m 1 "const_cast" || true` if [ ${HAS_CONST_CAST} ] && [ "${GIT_PR_ID}" != "" ]; then APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \ - python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641` + python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 21351065 3048612 46782768 30176695 12538138 6836917 32832641` echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}" if [ "${APPROVALS}" == "FALSE" ]; then - echo "You must have one RD (velconia,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE} for the avoidance of the bad C++ code habits." + echo "You must have one RD (XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao) approval for the api change! ${API_FILE} for the avoidance of the bad C++ code habits." exit 1 fi fi @@ -634,9 +666,7 @@ function card_test() { set +m } -function parallel_test() { - mkdir -p ${PADDLE_ROOT}/build - cd ${PADDLE_ROOT}/build +function parallel_test_base() { if [ ${WITH_TESTING:-ON} == "ON" ] ; then cat < ${FLUID_CORE} DEPENDS paddle_pybind) -add_custom_target(copy_paddle_pybind ALL DEPENDS ${FLUID_CORE}) +add_custom_target(copy_paddle_pybind ALL DEPENDS ${FLUID_CORE_DEPS}) IF(WIN32) add_custom_command(OUTPUT ${PADDLE_PYTHON_BUILD_DIR}/.timestamp diff --git a/python/paddle/dataset/flowers.py b/python/paddle/dataset/flowers.py index 5728a37fc33467968ca68de316d963f31f66da03..969ad3c922f9c15b2e39f71ae4359cd3d2fcdcce 100644 --- a/python/paddle/dataset/flowers.py +++ b/python/paddle/dataset/flowers.py @@ -117,26 +117,28 @@ def reader_creator(data_file, def reader(): while True: - for file in open(file_list): - file = file.strip() - batch = None - with open(file, 'rb') as f: - if six.PY2: - batch = pickle.load(f) - else: - batch = pickle.load(f, encoding='bytes') - if six.PY3: - batch = cpt.to_text(batch) - data = batch['data'] - labels = batch['label'] - for sample, label in six.moves.zip(data, batch['label']): - yield sample, int(label) - 1 + with open(file_list, 'r') as f_list: + for file in f_list: + file = file.strip() + batch = None + with open(file, 'rb') as f: + if six.PY2: + batch = pickle.load(f) + else: + batch = pickle.load(f, encoding='bytes') + + if six.PY3: + batch = cpt.to_text(batch) + data_batch = batch['data'] + labels_batch = batch['label'] + for sample, label in six.moves.zip(data_batch, + labels_batch): + yield sample, int(label) - 1 if not cycle: break if use_xmap: - cpu_num = int(os.environ.get('CPU_NUM', cpu_count())) - return xmap_readers(mapper, reader, cpu_num, buffered_size) + return xmap_readers(mapper, reader, min(4, cpu_count()), buffered_size) else: return map_readers(mapper, reader) diff --git a/python/paddle/dataset/mnist.py b/python/paddle/dataset/mnist.py index 847ca187206f8932e5454ddad881a94910efb55f..8dae48fae18734f998c31cc88e6e5c3e1b314bdd 100644 --- a/python/paddle/dataset/mnist.py +++ b/python/paddle/dataset/mnist.py @@ -78,7 +78,10 @@ def reader_creator(image_filename, label_filename, buffer_size): buffer_size, rows * cols)).astype('float32') offset_img += struct.calcsize(fmt_images) - images = images / 255.0 * 2.0 - 1.0 + images = images / 255.0 + images = images * 2.0 + images = images - 1.0 + for i in range(buffer_size): yield images[i, :], int(labels[i]) @@ -90,7 +93,7 @@ def train(): MNIST training set creator. It returns a reader creator, each sample in the reader is image pixels in - [0, 1] and label in [0, 9]. + [-1, 1] and label in [0, 9]. :return: Training reader creator :rtype: callable @@ -107,7 +110,7 @@ def test(): MNIST test set creator. It returns a reader creator, each sample in the reader is image pixels in - [0, 1] and label in [0, 9]. + [-1, 1] and label in [0, 9]. :return: Test reader creator. :rtype: callable diff --git a/python/paddle/dataset/wmt16.py b/python/paddle/dataset/wmt16.py index 1052d24c57b79e1db921f59bb6ea6ecdc87a7f81..770efe03a807f53a1dee3af1e740643c5f2303ee 100644 --- a/python/paddle/dataset/wmt16.py +++ b/python/paddle/dataset/wmt16.py @@ -48,8 +48,7 @@ __all__ = [ "get_dict", ] -DATA_URL = ("http://cloud.dlnel.org/filepub/" - "?uuid=46a0808e-ddd8-427c-bacd-0dbc6d045fed") +DATA_URL = ("http://paddlemodels.bj.bcebos.com/wmt/wmt16.tar.gz") DATA_MD5 = "0c38be43600334966403524a40dcd81e" TOTAL_EN_WORDS = 11250 diff --git a/python/paddle/distributed/launch.py b/python/paddle/distributed/launch.py index d8153fa00267b00eedc52aa043af9ba7dc090f7d..06369ea6b701ec2edac781f56dd76a20cff6e6e4 100644 --- a/python/paddle/distributed/launch.py +++ b/python/paddle/distributed/launch.py @@ -1,4 +1,4 @@ -# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,87 +11,58 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +""" +paddle.distributed.launch is a module that spawns multiple distributed +process on each trainning node for gpu trainning. + +Usage: + In both of single node training or multiple node training, this module +launch a process on each of the given gpu card. + + 1. for single node trainning with all visible gpu cards: + python -m paddle.distributed.launch \ + your_training_py (arg1 arg2 and all others) + + 2. for single node trainning with [0,4) cards + python -m paddle.distributed.launch --selected_gpus="0,1,2,3" \ + your_training_py (arg1 arg2 and all others) + + 3. for mulitple node training such as two node:192.168.0.16, 192.168.0.17 + on 192.168.0.16: + python -m paddle.distributed.launch --cluster_node_ips="192.168.0.16,192.168.0.17" \ + --node_ip=192.168.0.16 \ + your_training_py (arg1 arg2 and all others) + + on 192.168.0.17: + python -m paddle.distributed.launch --cluster_node_ips="192.168.0.16,192.168.0.17" \ + --node_ip=192.168.0.17 \ + your_training_py (arg1 arg2 and all others) +""" from __future__ import print_function - +import sys +from sys import version import subprocess import os -import sys -import time -import argparse - -default_envs = { - "PADDLE_TRAINER_ENDPOINTS": - "127.0.0.1:6170,127.0.0.1:6171,127.0.0.1:6172,127.0.0.1:6173,127.0.0.1:6174,127.0.0.1:6175,127.0.0.1:6176,127.0.0.1:6177", - "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""), - "PATH": os.getenv("PATH"), - "LD_PRELOAD": os.getenv("LD_PRELOAD", ""), - "PADDLE_TRAINERS_NUM": "8", - "NCCL_DEBUG": "INFO", - "GLOG_v": "0", - "NCCL_SOCKET_IFNAME": "eth0", - "NCCL_IB_GID_INDEX": "3", - "NCCL_IB_RETRY_CNT": "0", - "PYTHONPATH": os.getenv("PYTHONPATH", ""), -} - -GPUS = 8 - - -def start_procs(gpus, entrypoint, entrypoint_args, log_dir): - procs = [] - log_fns = [] - os.system("mkdir -p %s" % log_dir) - # ======== update parent envs ======= - for k, v in os.environ.items(): - if k.startswith("FLAGS_") or k.startswith("NCCL_") or \ - k.startswith("GLOG_"): - default_envs[k] = v - - # ======== for dist training ======= - node_trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0")) - current_ip = os.getenv("POD_IP", "127.0.0.1") - trainer_ips = os.getenv("PADDLE_TRAINERS", current_ip).split(",") - num_nodes = len(trainer_ips) - all_nodes_devices_endpoints = "" - for n in trainer_ips: - for i in range(gpus): - if all_nodes_devices_endpoints: - all_nodes_devices_endpoints += "," - all_nodes_devices_endpoints += "%s:617%d" % (n, i) - nranks = num_nodes * gpus - # ======== for dist training ======= - - for i in range(gpus): - curr_env = {} - curr_env.update(default_envs) - curr_env.update({ - "FLAGS_selected_gpus": "%d" % i, - "PADDLE_TRAINER_ID": "%d" % (node_trainer_id * gpus + i), - "PADDLE_CURRENT_ENDPOINT": "%s:617%d" % (current_ip, i), - # nranks - "PADDLE_TRAINERS_NUM": "%d" % nranks, - "PADDLE_TRAINER_ENDPOINTS": all_nodes_devices_endpoints - }) - - print("starting process ", i, entrypoint, entrypoint_args, curr_env) - fn = open("%s/workerlog.%d" % (log_dir, i), "w") - log_fns.append(fn) - cmd = [sys.executable, "-u", entrypoint] + entrypoint_args - procs.append(subprocess.Popen(cmd, stdout=fn, stderr=fn, env=curr_env)) +import six +import copy +from argparse import ArgumentParser, REMAINDER +import paddle.fluid as fluid - for i in range(gpus): - try: - procs[i].communicate() - procs[i].terminate() - log_fns[i].close() - except: - pass +def _print_arguments(args): + print("----------- Configuration Arguments -----------") + for arg, value in sorted(six.iteritems(vars(args))): + print("%s: %s" % (arg, value)) + print("------------------------------------------------") -def parse_args(): - parser = argparse.ArgumentParser( +def _parse_args(): + """ + Helper function parsing the command line options + @retval ArgumentParser + """ + parser = ArgumentParser( description='''start paddle training using multi-process mode. NOTE: your train program ***must*** run as distributed nccl2 mode, see: http://www.paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2- @@ -104,33 +75,148 @@ PADDLE_TRAINERS_NUM PADDLE_TRAINER_ENDPOINTS POD_IP (current node ip address, not needed for local training) ''') + + # Optional arguments for the launch helper parser.add_argument( - '--gpus', + "--cluster_node_ips", + type=str, + default="127.0.0.1", + help="Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17..") + + parser.add_argument( + "--node_ip", + type=str, + default="127.0.0.1", + help="The current node ip. ") + + parser.add_argument( + "--started_port", type=int, - default=8, - help='start number of processes for every gpu') + default=6170, + help="The trainer's started port on a single node") + + parser.add_argument( + "--print_config", + type=bool, + default=True, + help="Print the config or not") + parser.add_argument( - '--log_dir', + "--selected_gpus", type=str, - default="mylog", - help='directory to put logs per process.') + default=None, + help="It's for gpu trainning and the trainning process will run on the selected_gpus," + "each process is bound to a single GPU. And if it's not setted, this module will use all the gpu cards for training." + ) + parser.add_argument( - 'entrypoint_script', + "--log_dir", type=str, - help="The entrypoint script to be launched in parallel," - "followed by all the arguments for each process," - "e.g. train.py --lr 0.1") - parser.add_argument('entrypoint_args', nargs=argparse.REMAINDER) + help="The path for each process's log.If it's not setted, the log will printed to default pipe." + ) + + # positional + parser.add_argument( + "training_script", + type=str, + help="The full path to the single GPU training " + "program/script to be launched in parallel, " + "followed by all the arguments for the " + "training script") + + # rest from the training program + parser.add_argument('training_script_args', nargs=REMAINDER) return parser.parse_args() -def main(): - args = parse_args() +def start_procs(args): + """ + """ + procs = [] + log_fns = [] + + default_env = os.environ.copy() + + current_node_ip = args.node_ip + node_ips = [x.strip() for x in args.cluster_node_ips.split(',')] + node_id = node_ips.index(current_node_ip) + num_nodes = len(node_ips) + + if args.selected_gpus is None: + gpus_num = fluid.core.get_cuda_device_count() + selected_gpus = [str(x) for x in range(0, gpus_num)] + else: + selected_gpus = [x.strip() for x in args.selected_gpus.split(',')] + selected_gpus_num = len(selected_gpus) + + trainers_endpoints = "" + for ip in node_ips: + for i in range(selected_gpus_num): + if trainers_endpoints != "": + trainers_endpoints += "," + trainers_endpoints += "%s:617%d" % (ip, i) + + nranks = num_nodes * selected_gpus_num + + if args.print_config: + print("trainers_endpoints:", trainers_endpoints, ", node_id:", node_id, + ", current_node_ip:", current_node_ip, ", num_nodes:", num_nodes, + ", node_ips:", node_ips, ", nranks:", nranks) + + current_env = copy.copy(default_env) + # paddle broadcast ncclUniqueId use socket, and + # proxy maybe make trainers unreachable, so delete them. + # if we set them to "", grpc will log error message "bad uri" + # so just delete them. + current_env.pop("http_proxy", None) + current_env.pop("https_proxy", None) + + procs = [] + cmds = [] + for i in range(0, selected_gpus_num): + current_env.update({ + "FLAGS_selected_gpus": "%s" % selected_gpus[i], + "PADDLE_TRAINER_ID": "%d" % (node_id * selected_gpus_num + i), + "PADDLE_CURRENT_ENDPOINT": + "%s:%d" % (current_node_ip, args.started_port + i), + "PADDLE_TRAINERS_NUM": "%d" % nranks, + "PADDLE_TRAINER_ENDPOINTS": trainers_endpoints + }) + + cmd = [sys.executable, "-u", args.training_script + ] + args.training_script_args + + cmds.append(cmd) + + if args.log_dir is not None: + os.system("mkdir -p {}".format(args.log_dir)) + fn = open("%s/workerlog.%d" % (args.log_dir, i), "w") + log_fns.append(fn) + + proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn) + else: + proc = subprocess.Popen(cmd, env=current_env) + + procs.append(proc) + + for i in range(0, len(procs)): + proc = procs[i] + + proc.wait() + if len(log_fns) > 0: + log_fns[i].close() + + if proc.returncode != 0: + raise subprocess.CalledProcessError( + returncode=procs[i].returncode, cmd=cmds[i]) + - # launch multiple training process - start_procs(args.gpus, args.entrypoint_script, args.entrypoint_args, - args.log_dir) +def launch(): + args = _parse_args() + if args.print_config: + _print_arguments(args) + start_procs(args) if __name__ == "__main__": - main() + launch() diff --git a/python/paddle/fluid/__init__.py b/python/paddle/fluid/__init__.py index adc7c23f45a3b0a39272fa7d5b2bcab51bb8c690..1a3a1dd5096381691d086849cb9f68f6641518ba 100644 --- a/python/paddle/fluid/__init__.py +++ b/python/paddle/fluid/__init__.py @@ -39,6 +39,7 @@ from . import contrib from . import nets from . import optimizer from . import backward +from .backward import gradients from . import regularizer from . import average from . import metrics @@ -54,6 +55,7 @@ from .transpiler import DistributeTranspiler, \ memory_optimize, release_memory, DistributeTranspilerConfig from .lod_tensor import create_lod_tensor, create_random_int_lodtensor from . import clip +from . import dygraph_grad_clip from . import profiler from . import unique_name from . import recordio_writer @@ -71,7 +73,7 @@ Tensor = LoDTensor __all__ = framework.__all__ + executor.__all__ + \ trainer_desc.__all__ + inferencer.__all__ + transpiler.__all__ + \ parallel_executor.__all__ + lod_tensor.__all__ + \ - data_feed_desc.__all__ + compiler.__all__ + [ + data_feed_desc.__all__ + compiler.__all__ + backward.__all__ + [ 'io', 'initializer', 'layers', @@ -93,6 +95,7 @@ __all__ = framework.__all__ + executor.__all__ + \ 'WeightNormParamAttr', 'DataFeeder', 'clip', + 'dygraph_grad_clip', 'profiler', 'unique_name', 'recordio_writer', @@ -139,8 +142,8 @@ def __bootstrap__(): 'allocator_strategy', 'reader_queue_speed_test_mode', 'print_sub_graph_dir', 'pe_profile_fname', 'inner_op_parallelism', 'enable_parallel_graph', 'fuse_parameter_groups_size', - 'multiple_of_cupti_buffer_size', 'enable_subgraph_optimize', - 'fuse_parameter_memory_size', 'tracer_profile_fname' + 'multiple_of_cupti_buffer_size', 'fuse_parameter_memory_size', + 'tracer_profile_fname', 'dygraph_debug' ] if 'Darwin' not in sysstr: read_env_flags.append('use_pinned_memory') @@ -167,7 +170,7 @@ def __bootstrap__(): # env for communicator read_env_flags.append('communicator_independent_recv_thread') read_env_flags.append('communicator_send_queue_size') - read_env_flags.append('communicator_max_send_grad_num_before_recv') + read_env_flags.append('communicator_min_send_grad_num_before_recv') read_env_flags.append('communicator_thread_pool_size') read_env_flags.append('communicator_max_merge_var_num') read_env_flags.append('communicator_fake_rpc') @@ -182,8 +185,8 @@ def __bootstrap__(): 'fraction_of_gpu_memory_to_use', 'initial_gpu_memory_in_mb', 'reallocate_gpu_memory_in_mb', 'cudnn_deterministic', 'enable_cublas_tensor_op_math', 'conv_workspace_size_limit', - 'cudnn_exhaustive_search', 'memory_optimize_debug', 'selected_gpus', - 'sync_nccl_allreduce', 'limit_of_tmp_allocation', + 'cudnn_exhaustive_search', 'selected_gpus', 'sync_nccl_allreduce', + 'limit_of_tmp_allocation', 'times_excess_than_required_tmp_allocation', 'enable_inplace_whitelist', 'cudnn_batchnorm_spatial_persistent' ] diff --git a/python/paddle/fluid/backward.py b/python/paddle/fluid/backward.py index 41f9016edcb0964b4a95c10e257d10d548306ee8..9de001849b9a875f215d2bd7bee5b9485b1d0d78 100644 --- a/python/paddle/fluid/backward.py +++ b/python/paddle/fluid/backward.py @@ -22,7 +22,7 @@ import six from .. import compat as cpt from . import unique_name -__all__ = ['append_backward'] +__all__ = ['append_backward', 'gradients'] def _rename_arg_(op_descs, old_name, new_name, begin_idx=None, end_idx=None): @@ -142,6 +142,7 @@ def _addup_repetitive_outputs_(op_descs): pending_sum_ops = [] var_rename_count = collections.defaultdict(int) renamed_vars = collections.defaultdict(list) + renamed_var_start_idx = collections.defaultdict(list) for idx, op_desc in enumerate(op_descs): for var_name in op_desc.input_arg_names(): if len(renamed_vars[var_name]) > 1: @@ -159,6 +160,7 @@ def _addup_repetitive_outputs_(op_descs): if len(renamed_vars[var_name]) == 0: # it's the first time we get the variable renamed_vars[var_name] = [var_name] + renamed_var_start_idx[var_name] = idx else: if len(renamed_vars[var_name]) == 1: new_name = var_name + "@RENAME@" + \ @@ -166,7 +168,12 @@ def _addup_repetitive_outputs_(op_descs): var_rename_count[var_name] += 1 # rename original var_name renamed_vars[var_name][0] = new_name - _rename_arg_(op_descs, var_name, new_name, 0, idx) + # before change: _rename_arg_(op_descs, var_name, + # new_name, 0, idx) + # rename arg from idx of the first appearance + # in backward, not always from 0 + _rename_arg_(op_descs, var_name, new_name, + renamed_var_start_idx[var_name], idx) _rename_arg_(pending_sum_ops, var_name, new_name) for p in op_desc.output_names()[:param_idx]: @@ -232,15 +239,8 @@ def _remove_no_grad_branch_(op_descs, no_grad_set): for arg in op_desc.input_arg_names(): if core.grad_var_suffix() in arg and arg in no_grad_set: x_in = _strip_grad_suffix_(arg) - x_in_var_desc = op_desc.block().find_var_recursive( - cpt.to_bytes(x_in)) - assert x_in_var_desc is not None, "Variable {} not found".format( - x_in) - dtype = x_in_var_desc.dtype() - - to_insert.append( - (_create_op_desc_("fill_zeros_like2", {"X": [x_in]}, - {"Out": [arg]}, {"dtype": dtype}), idx)) + to_insert.append((_create_op_desc_( + "fill_zeros_like", {"X": [x_in]}, {"Out": [arg]}, {}), idx)) list([op_descs.insert(p[1], p[0]) for p in reversed(to_insert)]) @@ -261,7 +261,8 @@ def _append_backward_ops_(block, target_block, no_grad_dict, grad_to_var, - callbacks=None): + callbacks=None, + input_grad_names_set=None): """ Create all grad ops, and insert them into given block @@ -293,8 +294,13 @@ def _append_backward_ops_(block, sub_block = program.block(op._block_attr_id("sub_block")) grad_sub_block = program._create_block() grad_sub_block._set_forward_block_idx(sub_block.idx) + # see follwing comments for why set None here. + pre_input_grad_names_set = copy.copy(input_grad_names_set) + input_grad_names_set = None _append_backward_ops_(sub_block, sub_block.ops, grad_sub_block, - no_grad_dict, grad_to_var, callbacks) + no_grad_dict, grad_to_var, callbacks, + input_grad_names_set) + input_grad_names_set = pre_input_grad_names_set program._rollback() grad_sub_block_list.append(grad_sub_block.desc) @@ -303,8 +309,33 @@ def _append_backward_ops_(block, grad_op_desc, op_grad_to_var = core.get_grad_op_desc( op.desc, cpt.to_text(no_grad_dict[block.idx]), grad_sub_block_list) - grad_op_descs.extend(grad_op_desc) - grad_to_var.update(op_grad_to_var) + # If input_grad_names_set is not None, extend grad_op_descs only when + # any input grad in outputs of previous grad ops. + # But this strategy is not suited for while op for some control flow, + # for example, for while op, the grads maybe generated in next loop. + if input_grad_names_set is not None: + is_append_grad = False + for op_desc in grad_op_desc: + input_grad_names = [ + name for name in op_desc.input_arg_names() + if name.find(core.grad_var_suffix()) != -1 + ] + # some code of gradient ops, like increment, are not very + # standard, there is no @GRAD in these ops' inputs. + if len(input_grad_names) == 0: + is_append_grad = True + break + + if _some_in_set_(input_grad_names, input_grad_names_set): + grad_op_descs.append(op_desc) + is_append_grad = True + for name in op_desc.output_arg_names(): + input_grad_names_set.add(name) + if is_append_grad: + grad_to_var.update(op_grad_to_var) + else: + grad_op_descs.extend(grad_op_desc) + grad_to_var.update(op_grad_to_var) grad_op_descs = _addup_repetitive_outputs_(grad_op_descs) @@ -488,6 +519,8 @@ def append_backward(loss, parameter_list=None, no_grad_set=None, isinstance(callbacks, list) program = loss.block.program + program._appending_grad_times += 1 + if no_grad_set is None: no_grad_set = set() no_grad_set = copy.copy(no_grad_set) @@ -518,10 +551,23 @@ def append_backward(loss, parameter_list=None, no_grad_set=None, block_no_grad_set = set(map(_strip_grad_suffix_, no_grad_dict[0])) op_path = _find_op_path_(root_block, [loss], [], block_no_grad_set) + no_grad_dict[0].update(list(map(_append_grad_suffix_, block_no_grad_set))) - _append_backward_ops_(root_block, op_path, root_block, no_grad_dict, - grad_to_var, callbacks) + input_grad_names_set = None + # For double backward, input_grad_names is used for filter + # some non-used gradients op. + if program._appending_grad_times > 1: + input_grad_names_set = set([_append_grad_suffix_(loss.name)]) + + _append_backward_ops_( + root_block, + op_path, + root_block, + no_grad_dict, + grad_to_var, + callbacks, + input_grad_names_set=input_grad_names_set) # Because calc_gradient may be called multiple times, # we need rename the internal gradient variables so that they have @@ -625,17 +671,20 @@ def _find_op_path_(block, outputs, inputs, no_grad_set): def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None): """ - Backpropagate the graidents of targets to inputs. + Backpropagate the gradients of targets to inputs. Args: targets(Variable|list[Variable]): The target variables inputs(Variable|list[Variable]): The input variables + target_gradients (Variable|list[Variable]|None): The gradient variables + of targets which has the same shape with targets, If None, ones will + be created for them. no_grad_set(set[string]): The names of variables that have no gradients in Block 0. All variables with `stop_gradient=True` from all blocks will be automatically added. Return: - (list[Variable]): list of gradients for inputs + (list[Variable]): A list of gradients for inputs If an input does not affect targets, the corresponding gradient variable will be None """ @@ -645,6 +694,8 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None): block = targets[0].block prog = block.program + # increase appending gradients times + prog._appending_grad_times += 1 block_idx = block.idx if not target_gradients: @@ -662,6 +713,8 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None): fwd_op_num = block.desc.op_size() + input_grad_names_set = set() + target_grad_map = {} for i, grad in enumerate(target_gradients): target = targets[i] @@ -677,6 +730,7 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None): 'output_dim_idx': 0 }) block.desc.append_op().copy_from(op_desc) + input_grad_names_set.add(grad_name) else: if target.block.idx != block_idx or target.block.program != prog: raise ValueError("all targets must be in the same block") @@ -685,6 +739,12 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None): "The shapes of target and grad are different: %s %s" % ( target.name, grad.name)) target_grad_map[_append_grad_suffix_(target.name)] = grad.name + input_grad_names_set.add(grad.name) + + # For double backward, input_grad_names is used for filter + # some non-used gradients op. + if prog._appending_grad_times == 1: + input_grad_names_set = None for input in inputs: if input.block.program != prog: @@ -695,7 +755,13 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None): no_grad_dict[0].update(list(map(_append_grad_suffix_, block_no_grad_set))) grad_to_var = dict() grad_info_map = dict() - _append_backward_ops_(block, op_path, block, no_grad_dict, grad_to_var) + _append_backward_ops_( + block, + op_path, + block, + no_grad_dict, + grad_to_var, + input_grad_names_set=input_grad_names_set) # Because calc_gradient may be called multiple times, # we need rename the internal gradient variables so that they have @@ -719,3 +785,40 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None): return grad_vars[0] else: return grad_vars + + +def gradients(targets, inputs, target_gradients=None, no_grad_set=None): + """ + Backpropagate the gradients of targets to inputs. + + Args: + targets (Variable|list[Variable]): The target variables. + inputs (Variable|list[Variable]): The input variables. + target_gradients (Variable|list[Variable]|None): The gradient variables + of targets which has the same shape with targets, If None, ones will + be created for them. + no_grad_set (set[string]): The names of variables that have no gradients + in Block 0. All variables with `stop_gradient=True` from all blocks + will be automatically added. + + Return: + (list[Variable]): A list of gradients for inputs + If an input does not affect targets, the corresponding gradient variable + will be None. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + x = fluid.layers.data(name='x', shape=[2,8,8], dtype='float32') + x.stop_gradient=False + y = fluid.layers.conv2d(x, 4, 1, bias_attr=False) + y = fluid.layers.relu(y) + y = fluid.layers.conv2d(y, 4, 1, bias_attr=False) + y = fluid.layers.relu(y) + z = fluid.gradients([y], x) + print(z) + """ + outs = calc_gradient(targets, inputs, target_gradients, no_grad_set) + return _as_list(outs) diff --git a/python/paddle/fluid/clip.py b/python/paddle/fluid/clip.py index 0f7dd531b3e5992caa558def6bbdf446a7d2ffaa..1c51ef296c6a2d0d1cc21bf55187c1f0722570ff 100644 --- a/python/paddle/fluid/clip.py +++ b/python/paddle/fluid/clip.py @@ -21,6 +21,7 @@ import functools from . import layers from . import framework from . import core +from .dygraph import not_support __all__ = [ 'ErrorClipByValue', @@ -55,7 +56,23 @@ class ErrorClipByValue(BaseErrorClipAttr): Examples: .. code-block:: python - var = fluid.framework.Variable(..., error_clip=ErrorClipByValue(max=5.0), ...) + import paddle.fluid as fluid + BATCH_SIZE = 128 + CLIP_MAX = 2e-6 + CLIP_MIN = -1e-6 + prog = fluid.framework.Program() + with fluid.program_guard(main_program=prog): + image = fluid.layers.data(name='x', shape=[784], dtype='float32') + hidden1 = fluid.layers.fc(input=image, size=128, act='relu') + hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu') + predict = fluid.layers.fc(input=hidden2, size=10, act='softmax') + label = fluid.layers.data(name='y', shape=[1], dtype='int64') + cost = fluid.layers.cross_entropy(input=predict, label=label) + avg_cost = fluid.layers.mean(cost) + prog_clip = prog.clone() + prog_clip.block(0).var(hidden1.name)._set_error_clip( + fluid.clip.ErrorClipByValue( + max=CLIP_MAX, min=CLIP_MIN) """ def __init__(self, max, min=None): @@ -134,12 +151,14 @@ class GradientClipByValue(BaseGradientClipAttr): Examples: .. code-block:: python + import paddle.fluid as fluid w_param_attrs = fluid.ParamAttr(name=None, initializer=fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0), learning_rate=1.0, regularizer=fluid.regularizer.L1Decay(1.0), trainable=True, - clip=fluid.clip.GradientClipByValue(-1.0, 1.0)) + gradient_clip=fluid.clip.GradientClipByValue(-1.0, 1.0)) + x = fluid.layers.data(name='x', shape=[10], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs) """ @@ -185,12 +204,14 @@ class GradientClipByNorm(BaseGradientClipAttr): Examples: .. code-block:: python - w_param_attrs = flui.ParamAttr(name=None, + import paddle.fluid as fluid + w_param_attrs = fluid.ParamAttr(name=None, initializer=fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0), learning_rate=1.0, regularizer=fluid.regularizer.L1Decay(1.0), trainable=True, - clip=fluid.clip.GradientClipByNorm(clip_norm=2.0)) + gradient_clip=fluid.clip.GradientClipByNorm(clip_norm=2.0)) + x = fluid.layers.data(name='x', shape=[10], dtype='float32') y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs) """ @@ -239,6 +260,20 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr): Examples: .. code-block:: python + import paddle.fluid as fluid + prog = fluid.framework.Program() + startup_program = fluid.framework.Program() + with fluid.program_guard( + main_program=prog, startup_program=startup_program): + image = fluid.layers.data(name='x', shape=[784], dtype='float32') + label = fluid.layers.data(name='y', shape=[1], dtype='int64') + hidden1 = fluid.layers.fc(input=image, size=128, act='relu') + hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu') + predict = fluid.layers.fc(input=hidden2, size=10, act='softmax') + cost = fluid.layers.cross_entropy(input=predict, label=label) + avg_cost = fluid.layers.mean(cost) + prog_clip = prog.clone() + avg_cost_clip = prog_clip.block(0).var(avg_cost.name) p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip) with fluid.program_guard(main_program=prog_clip): @@ -301,6 +336,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr): return param, new_grad +@not_support def set_gradient_clip(clip, param_list=None, program=None): """ To specify parameters that require gradient clip. diff --git a/python/paddle/fluid/compiler.py b/python/paddle/fluid/compiler.py index f01a6dd9da2dd518227d0f45bab9a140191d38de..87a6ce0881f4371d5fd49ff1e1753546fb93b56e 100644 --- a/python/paddle/fluid/compiler.py +++ b/python/paddle/fluid/compiler.py @@ -98,6 +98,7 @@ class CompiledProgram(object): def __init__(self, program_or_graph): if isinstance(program_or_graph, core.Graph): self._graph = program_or_graph + # don't not create a new program here. self._program = None elif isinstance(program_or_graph, framework.Program): self._graph = core.Graph(program_or_graph.desc) @@ -106,7 +107,6 @@ class CompiledProgram(object): raise ValueError("Wrong program_to_graph type: %s" % type(program_or_graph)) - self._program_desc = self._graph.origin_program_desc() self._scope = None self._place = None self._executor = None @@ -299,6 +299,7 @@ class CompiledProgram(object): # TODO(wuyi): trainer endpoings should be passed in through # build_strategy, not program.xxx. + # TODO(gongwb): let user to set them once. if self._program and self._build_strategy.num_trainers > 1 and \ self._program._trainers_endpoints: tps = self._program._trainers_endpoints @@ -307,6 +308,12 @@ class CompiledProgram(object): tps), "num_trainers == len(end_points)" self._build_strategy.trainers_endpoints = tps + if self._program: + self._build_strategy.nccl_comm_num = self._program._nccl_comm_num + self._build_strategy.use_hierarchical_allreduce_ = self._program._use_hierarchical_allreduce + self._build_strategy.hierarchical_allreduce_inter_nranks_ = self._program._hierarchical_allreduce_inter_nranks + self._build_strategy.hierarchical_allreduce_exter_nranks_ = self._program._hierarchical_allreduce_exter_nranks + if self._build_strategy.sync_batch_norm: self._build_strategy.enable_sequential_execution = True diff --git a/python/paddle/fluid/contrib/__init__.py b/python/paddle/fluid/contrib/__init__.py index f808f30bba4b1940a2c82ced88b427f9112405c5..72437c0138fba692ea1e202c19fe2b5a75f11080 100644 --- a/python/paddle/fluid/contrib/__init__.py +++ b/python/paddle/fluid/contrib/__init__.py @@ -36,6 +36,8 @@ from . import model_stat from .model_stat import * from . import mixed_precision from .mixed_precision import * +from . import layers +from .layers import * __all__ = [] __all__ += decoder.__all__ @@ -48,3 +50,4 @@ __all__ += slim.__all__ __all__ += utils.__all__ __all__ += extend_optimizer.__all__ __all__ += ['mixed_precision'] +__all__ += layers.__all__ diff --git a/python/paddle/fluid/contrib/int8_inference/README.md b/python/paddle/fluid/contrib/int8_inference/README.md index 3228610f968c9bec86d6bf781585038ffd095bce..7dc7c8d2a374a1d589ccb072b5bf6cce1f6ddda7 100644 --- a/python/paddle/fluid/contrib/int8_inference/README.md +++ b/python/paddle/fluid/contrib/int8_inference/README.md @@ -6,7 +6,7 @@ PaddlePaddle supports offline INT8 calibration to accelerate the inference speed You need to install at least PaddlePaddle-1.3 python package `pip install paddlepaddle==1.3`. ## 1. How to generate INT8 model -You can refer to the unit test in [test_calibration.py](../tests/test_calibration.py). Basically, there are three steps: +You can refer to the unit test in [test_calibration_resnet50.py](../tests/test_calibration_resnet50.py). Basically, there are three steps: * Construct calibration object. ```python @@ -68,18 +68,19 @@ Notes: * The INT8 theoretical speedup is 4X on Intel® Xeon® Cascadelake Server (please refer to `The theoretical peak compute gains are 4x int8 OPS over fp32 OPS.` in [Reference](https://software.intel.com/en-us/articles/lower-numerical-precision-deep-learning-inference-and-training "Reference")). Therefore, op-level gain is 4X and topology-level is smaller. ## 4. How to reproduce the results -* Small dataset (Single core) +* Small dataset for ResNet-50 (Single core) ```bash -FLAGS_use_mkldnn=true python python/paddle/fluid/contrib/tests/test_calibration.py +FLAGS_use_mkldnn=true python python/paddle/fluid/contrib/tests/test_calibration_resnet50.py ``` +>Note: Change `test_calibration_resnet50.py` to `test_calibration_mobilenetv1.py` for MobileNet-V1. Same for the following commands. -* Full dataset (Single core) +* Full dataset for ResNet-50 (Single core) ```bash -FLAGS_use_mkldnn=true DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py +FLAGS_use_mkldnn=true DATASET=full python python/paddle/fluid/contrib/tests/test_calibration_resnet50.py ``` -* Full dataset (Multi-core) +* Full dataset for ResNet-50 (Multi-core) ```bash -FLAGS_use_mkldnn=true OMP_NUM_THREADS=20 DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py +FLAGS_use_mkldnn=true OMP_NUM_THREADS=20 DATASET=full python python/paddle/fluid/contrib/tests/test_calibration_resnet50.py ``` > Notes: This is an example command with 20 cores by using set `OMP_NUM_THREADS` value. diff --git a/python/paddle/fluid/contrib/mixed_precision/decorator.py b/python/paddle/fluid/contrib/mixed_precision/decorator.py index f17b63434de9ed4b315dbb6618d762ecc19b245d..e07f6ce8ab70a4bbdc638b45e7caa8490c61eeca 100644 --- a/python/paddle/fluid/contrib/mixed_precision/decorator.py +++ b/python/paddle/fluid/contrib/mixed_precision/decorator.py @@ -18,6 +18,7 @@ from ... import layers from ... import unique_name from . import fp16_utils from .fp16_utils import create_master_params_grads, master_param_to_train_param +from .fp16_utils import update_loss_scaling __all__ = ["decorate"] @@ -35,15 +36,51 @@ class OptimizerWithMixedPrecison(object): optimizer (Optimizer): A common Optimizer object. init_loss_scaling (float): The initial loss scaling factor. use_dynamic_loss_scaling (bool): Whether to use dynamic loss scaling. + incr_every_n_steps(int): Increases loss scaling every n consecutive + steps with finite gradients. + decr_every_n_nan_or_inf(int): Decreases loss scaling every n + accumulated steps with nan or + inf gradients. + incr_ratio(float): The multiplier to use when increasing the loss + scaling. + decr_ratio(float): The less-than-one-multiplier to use when decreasing + the loss scaling. + """ - def __init__(self, optimizer, init_loss_scaling, use_dynamic_loss_scaling): + def __init__(self, optimizer, init_loss_scaling, use_dynamic_loss_scaling, + incr_every_n_steps, decr_every_n_nan_or_inf, incr_ratio, + decr_ratio): self._optimizer = optimizer self._param_grads = None self._train_program = default_main_program() self._startup_prog = default_startup_program() - self._loss_scaling = init_loss_scaling + self._loss_scaling = layers.create_global_var( + name=unique_name.generate("loss_scaling"), + shape=[1], + value=init_loss_scaling, + dtype='float32', + persistable=True) self._use_dynamic_loss_scaling = use_dynamic_loss_scaling + if self._use_dynamic_loss_scaling: + self._incr_every_n_steps = layers.fill_constant( + shape=[1], dtype='int32', value=incr_every_n_steps) + self._decr_every_n_nan_or_inf = layers.fill_constant( + shape=[1], dtype='int32', value=decr_every_n_nan_or_inf) + self._incr_ratio = incr_ratio + self._decr_ratio = decr_ratio + self._num_good_steps = layers.create_global_var( + name=unique_name.generate("num_good_steps"), + shape=[1], + value=0, + dtype='int32', + persistable=True) + self._num_bad_steps = layers.create_global_var( + name=unique_name.generate("num_bad_steps"), + shape=[1], + value=0, + dtype='int32', + persistable=True) # Ensure the data type of learning rate vars is float32 (same as the # master parameter dtype) @@ -104,9 +141,33 @@ class OptimizerWithMixedPrecison(object): Returns: A list of optimize operators. """ + + if self._use_dynamic_loss_scaling: + + grads = [layers.reduce_sum(g) for [_, g] in master_params_grads] + all_grads = layers.concat(grads) + all_grads_sum = layers.reduce_sum(all_grads) + is_overall_finite = layers.isfinite(all_grads_sum) + + update_loss_scaling(is_overall_finite, self._loss_scaling, + self._num_good_steps, self._num_bad_steps, + self._incr_every_n_steps, + self._decr_every_n_nan_or_inf, self._incr_ratio, + self._decr_ratio) + + # apply_gradient append all ops in global block, thus we shouldn't + # apply gradient in the switch branch. + with layers.Switch() as switch: + with switch.case(is_overall_finite): + pass + with switch.default(): + for _, g in master_params_grads: + layers.assign(layers.zeros_like(g), g) + optimize_ops = self._optimizer.apply_gradients(master_params_grads) master_param_to_train_param(master_params_grads, self._param_grads, self._train_program) + return optimize_ops def minimize(self, loss): @@ -126,13 +187,28 @@ class OptimizerWithMixedPrecison(object): return scaled_loss, optimize_ops, master_params_grads -def decorate(optimizer, init_loss_scaling=1.0, use_dynamic_loss_scaling=False): +def decorate(optimizer, + init_loss_scaling=1.0, + incr_every_n_steps=1000, + decr_every_n_nan_or_inf=2, + incr_ratio=2.0, + decr_ratio=0.8, + use_dynamic_loss_scaling=False): """ Decorate the given optimizer to adapt to the mixed-precision training. Args: optimizer(Optimizer): A common Optimizer. init_loss_scaling(float): The initial loss scaling factor. + incr_every_n_steps(int): Increases loss scaling every n consecutive + steps with finite gradients. + decr_every_n_nan_or_inf(int): Decreases loss scaling every n + accumulated steps with nan or + inf gradients. + incr_ratio(float): The multiplier to use when increasing the loss + scaling. + decr_ratio(float): The less-than-one-multiplier to use when decreasing + the loss scaling. use_dynamic_loss_scaling(bool): Whether to use dynamic loss scaling. Returns: @@ -151,7 +227,8 @@ def decorate(optimizer, init_loss_scaling=1.0, use_dynamic_loss_scaling=False): scaled_loss, _, _ = mp_optimizer.minimize(loss) """ - mp_optimizer = OptimizerWithMixedPrecison(optimizer, init_loss_scaling, - use_dynamic_loss_scaling) + mp_optimizer = OptimizerWithMixedPrecison( + optimizer, init_loss_scaling, use_dynamic_loss_scaling, + incr_every_n_steps, decr_every_n_nan_or_inf, incr_ratio, decr_ratio) return mp_optimizer diff --git a/python/paddle/fluid/contrib/mixed_precision/fp16_utils.py b/python/paddle/fluid/contrib/mixed_precision/fp16_utils.py index 5e7fdcedead2233b3b412abd9815301cf528f9af..3445cdbcbb496918400e5c56104f4edb9ef19a0b 100644 --- a/python/paddle/fluid/contrib/mixed_precision/fp16_utils.py +++ b/python/paddle/fluid/contrib/mixed_precision/fp16_utils.py @@ -91,15 +91,11 @@ def create_master_params_grads(params_grads, main_prog, startup_prog, append_cast_op(startup_p, startup_master_param, startup_prog) # cast fp16 gradients to fp32 before apply gradients if g.name.find("batch_norm") > -1: - if loss_scaling > 1: - scaled_g = g / float(loss_scaling) - else: - scaled_g = g + scaled_g = g / loss_scaling master_params_grads.append([p, scaled_g]) continue master_grad = layers.cast(x=g, dtype="float32") - if loss_scaling > 1: - master_grad = master_grad / float(loss_scaling) + master_grad = master_grad / loss_scaling master_params_grads.append([master_param, master_grad]) return master_params_grads @@ -123,3 +119,77 @@ def master_param_to_train_param(master_params_grads, params_grads, main_prog): with main_prog._optimized_guard([m_p_g[0], m_p_g[1]]): # fp32 -> fp16 append_cast_op(m_p_g[0], train_p, main_prog) + + +def update_loss_scaling(is_overall_finite, prev_loss_scaling, num_good_steps, + num_bad_steps, incr_every_n_steps, + decr_every_n_nan_or_inf, incr_ratio, decr_ratio): + """ + Update loss scaling according to overall gradients. If all gradients is + finite after incr_every_n_steps, loss scaling will increase by incr_ratio. + Otherwisw, loss scaling will decrease by decr_ratio after + decr_every_n_nan_or_inf steps and each step some gradients are infinite. + + Args: + is_overall_finite (Variable): A boolean variable indicates whether + all gradients are finite. + prev_loss_scaling (Variable): Previous loss scaling. + num_good_steps (Variable): A variable accumulates good steps in which + all gradients are finite. + num_bad_steps (Variable): A variable accumulates bad steps in which + some gradients are infinite. + incr_every_n_steps (Variable): A variable represents increasing loss + scaling every n consecutive steps with + finite gradients. + decr_every_n_nan_or_inf (Variable): A variable represents decreasing + loss scaling every n accumulated + steps with nan or inf gradients. + incr_ratio(float): The multiplier to use when increasing the loss + scaling. + decr_ratio(float): The less-than-one-multiplier to use when decreasing + loss scaling. + """ + zero_steps = layers.fill_constant(shape=[1], dtype='int32', value=0) + with layers.Switch() as switch: + with switch.case(is_overall_finite): + should_incr_loss_scaling = layers.less_than(incr_every_n_steps, + num_good_steps + 1) + with layers.Switch() as switch1: + with switch1.case(should_incr_loss_scaling): + new_loss_scaling = prev_loss_scaling * incr_ratio + loss_scaling_is_finite = layers.isfinite(new_loss_scaling) + with layers.Switch() as switch2: + with switch2.case(loss_scaling_is_finite): + layers.assign(new_loss_scaling, prev_loss_scaling) + with switch2.default(): + pass + layers.assign(zero_steps, num_good_steps) + layers.assign(zero_steps, num_bad_steps) + + with switch1.default(): + layers.increment(num_good_steps) + layers.assign(zero_steps, num_bad_steps) + + with switch.default(): + should_decr_loss_scaling = layers.less_than(decr_every_n_nan_or_inf, + num_bad_steps + 1) + with layers.Switch() as switch3: + with switch3.case(should_decr_loss_scaling): + new_loss_scaling = prev_loss_scaling * decr_ratio + static_loss_scaling = \ + layers.fill_constant(shape=[1], + dtype='float32', + value=1.0) + less_than_one = layers.less_than(new_loss_scaling, + static_loss_scaling) + with layers.Switch() as switch4: + with switch4.case(less_than_one): + layers.assign(static_loss_scaling, + prev_loss_scaling) + with switch4.default(): + layers.assign(new_loss_scaling, prev_loss_scaling) + layers.assign(zero_steps, num_good_steps) + layers.assign(zero_steps, num_bad_steps) + with switch3.default(): + layers.assign(zero_steps, num_good_steps) + layers.increment(num_bad_steps) diff --git a/python/paddle/fluid/contrib/reader/README.md b/python/paddle/fluid/contrib/reader/README.md index 9e4b7d1ce3d9664495220d7ccfc6ef6eac0b81c2..07c5430916a92c548f413b0069bdbf4d9896bb49 100644 --- a/python/paddle/fluid/contrib/reader/README.md +++ b/python/paddle/fluid/contrib/reader/README.md @@ -13,3 +13,13 @@ and two types of data format: * label dense_fea,dense_fea sparse_fea,sparse_fea - the svm data format is : * label slot1:fea_sign slot2:fea_sign slot1:fea_sign + +## Distributed reader + +The distributed reader is mainly used by multi-process tasks, it splits the origin batch samples to N sub-batch samples, and the N is equal to the number of processes. The usage is similar to `paddle.batch`. + +Cons: + - It can be operated conveniently so that different processes can read different data. + +Pros: + - Because each process reads the original batch data and then divides the data, the performance may be poor. diff --git a/python/paddle/fluid/contrib/reader/__init__.py b/python/paddle/fluid/contrib/reader/__init__.py index 4cf85ffc166420f117db9576b4d687c96d429e3c..e96acc5682a05606e0fbac689c9fbf87c28ad668 100644 --- a/python/paddle/fluid/contrib/reader/__init__.py +++ b/python/paddle/fluid/contrib/reader/__init__.py @@ -15,5 +15,8 @@ from __future__ import print_function from . import ctr_reader +from .distributed_reader import * -__all__ = ctr_reader.__all__ +__all__ = [] +__all__ += distributed_reader.__all__ +__all__ += ctr_reader.__all__ diff --git a/python/paddle/fluid/contrib/slim/core/compressor.py b/python/paddle/fluid/contrib/slim/core/compressor.py index b97508018ac6da47bfdefadd06a6c3788cb7bd77..2627f7f004bc47a5d1b2e5e22d7fe05373ae3ec8 100644 --- a/python/paddle/fluid/contrib/slim/core/compressor.py +++ b/python/paddle/fluid/contrib/slim/core/compressor.py @@ -12,12 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. -from ....core import CPUPlace +from ....core import CPUPlace, EOFException from .... import compiler +from ....framework import Variable from .... import io from .... import profiler from .... import scope_guard from ....data_feeder import DataFeeder +from ....log_helper import get_logger from ..graph import * from .config import ConfigFactory import numpy as np @@ -28,12 +30,12 @@ import logging import sys import pickle import functools +import traceback __all__ = ['Context', 'Compressor'] -logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s') -_logger = logging.getLogger(__name__) -_logger.setLevel(logging.INFO) +_logger = get_logger( + __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') def cached_reader(reader, sampled_rate, cache_path, cached_id): @@ -83,7 +85,8 @@ class Context(object): eval_reader=None, teacher_graphs=None, train_optimizer=None, - distiller_optimizer=None): + distiller_optimizer=None, + search_space=None): """ Args: place: The device place where the compression job running. @@ -119,6 +122,9 @@ class Context(object): self.cache_path = './eval_cache' self.eval_results = {} + self.skip_training = False + self.search_space = search_space + def to_file(self, file_name): """ Save the context into file. @@ -181,14 +187,30 @@ class Context(object): if sampled_rate: reader = cached_reader(reader, sampled_rate, self.cache_path, cached_id) - for data in reader(): - result = executor.run(eval_graph, self.scope, data=data) - result = [np.mean(r) for r in result] - results.append(result) - if batch_id % 20 == 0: - _logger.info("batch-{}; {}={}".format( - batch_id, eval_graph.out_nodes.keys(), result)) - batch_id += 1 + + if isinstance(reader, Variable): + reader.start() + try: + while True: + result = executor.run(eval_graph, self.scope) + result = [np.mean(r) for r in result] + results.append(result) + if batch_id % 20 == 0: + _logger.info("batch-{}; {}={}".format( + batch_id, eval_graph.out_nodes.keys(), result)) + batch_id += 1 + except EOFException: + reader.reset() + else: + for data in reader(): + result = executor.run(eval_graph, self.scope, data=data) + result = [np.mean(r) for r in result] + results.append(result) + if batch_id % 20 == 0: + _logger.info("batch-{}; {}={}".format( + batch_id, eval_graph.out_nodes.keys(), result)) + batch_id += 1 + result = np.mean(np.array(results), axis=0) _logger.info("Final eval result: {}={}".format( eval_graph.out_nodes.keys(), result)) @@ -221,9 +243,10 @@ class Compressor(object): eval_feed_list=None, eval_fetch_list=None, teacher_programs=[], - checkpoint_path='./checkpoints', + checkpoint_path=None, train_optimizer=None, - distiller_optimizer=None): + distiller_optimizer=None, + search_space=None): """ Args: place(fluid.Place): The device place where the compression job running. @@ -251,12 +274,14 @@ class Compressor(object): this optimizer is used to minimize the combined loss of student-net and teacher-net while train_optimizer is used to minimize loss of student-net in fine-tune stage. + search_space(slim.nas.SearchSpace): The instance that define the searching space. It must inherite + slim.nas.SearchSpace class and overwrite the abstract methods. """ - assert isinstance( + assert train_feed_list is None or isinstance( train_feed_list, list ), "train_feed_list should be a list of tuple, such as [('image', image.name), ('label', gt.name)]" - assert isinstance( + assert eval_feed_list is None or isinstance( eval_feed_list, list ), "eval_feed_list should be a list of tuple, such as [('image', image.name), ('label', gt.name)]" self.strategies = [] @@ -281,6 +306,8 @@ class Compressor(object): self.distiller_optimizer = distiller_optimizer self.init_model = None + self.search_space = search_space + def _add_strategy(self, strategy): """ Add a strategy to current compress pass. @@ -306,6 +333,9 @@ class Compressor(object): if 'init_model' in factory.compressor: self.init_model = factory.compressor['init_model'] + if 'eval_epoch' in factory.compressor: + self.eval_epoch = factory.compressor['eval_epoch'] + def _init_model(self, context): """ Load model that has been compressed. @@ -402,7 +432,8 @@ class Compressor(object): """ Train one epoch. """ - + if context.skip_training: + return executor = SlimGraphExecutor(self.place) if context.optimize_graph.compiled_graph is None: @@ -410,21 +441,44 @@ class Compressor(object): context.optimize_graph.program).with_data_parallel( loss_name=context.optimize_graph.out_nodes['loss']) - for data in context.train_reader(): - for strategy in self.strategies: - strategy.on_batch_begin(context) - results = executor.run(context.optimize_graph, - context.scope, - data=data) - results = [float(np.mean(result)) for result in results] - if context.batch_id % 20 == 0: - _logger.info("epoch:{}; batch_id:{}; {} = {}".format( - context.epoch_id, context.batch_id, - context.optimize_graph.out_nodes.keys( - ), [round(r, 3) for r in results])) - for strategy in self.strategies: - strategy.on_batch_end(context) - context.batch_id += 1 + if isinstance(context.train_reader, Variable): + context.train_reader.start() + try: + while True: + + for strategy in self.strategies: + strategy.on_batch_begin(context) + results = executor.run(context.optimize_graph, + context.scope) + results = [float(np.mean(result)) for result in results] + if context.batch_id % 20 == 0: + _logger.info("epoch:{}; batch_id:{}; {} = {}".format( + context.epoch_id, context.batch_id, + context.optimize_graph.out_nodes.keys( + ), [round(r, 3) for r in results])) + for strategy in self.strategies: + strategy.on_batch_end(context) + context.batch_id += 1 + + except EOFException: + context.train_reader.reset() + + else: + for data in context.train_reader(): + for strategy in self.strategies: + strategy.on_batch_begin(context) + results = executor.run(context.optimize_graph, + context.scope, + data=data) + results = [float(np.mean(result)) for result in results] + if context.batch_id % 20 == 0: + _logger.info("epoch:{}; batch_id:{}; {} = {}".format( + context.epoch_id, context.batch_id, + context.optimize_graph.out_nodes.keys( + ), [round(r, 3) for r in results])) + for strategy in self.strategies: + strategy.on_batch_end(context) + context.batch_id += 1 context.batch_id = 0 def _eval(self, context): @@ -450,7 +504,8 @@ class Compressor(object): eval_reader=self.eval_reader, teacher_graphs=self.teacher_graphs, train_optimizer=self.train_optimizer, - distiller_optimizer=self.distiller_optimizer) + distiller_optimizer=self.distiller_optimizer, + search_space=self.search_space) self.context = context if self.teacher_graphs: context.put('teachers', self.teacher_graphs) @@ -467,18 +522,25 @@ class Compressor(object): for strategy in self.strategies: strategy.on_compression_begin(context) + if 'MKLDNNPostTrainingQuantStrategy' in [ + i.__class__.__name__ for i in self.strategies + ]: + return None start = context.epoch_id - self._eval(context) for epoch in range(start, self.epoch): context.epoch_id = epoch - for strategy in self.strategies: - strategy.on_epoch_begin(context) - self._train_one_epoch(context) - for strategy in self.strategies: - strategy.on_epoch_end(context) - if self.eval_epoch and epoch % self.eval_epoch == 0: - self._eval(context) - self._save_checkpoint(context) + try: + for strategy in self.strategies: + strategy.on_epoch_begin(context) + self._train_one_epoch(context) + if self.eval_epoch and epoch % self.eval_epoch == 0: + self._eval(context) + self._save_checkpoint(context) + for strategy in self.strategies: + strategy.on_epoch_end(context) + except Exception: + _logger.error(traceback.print_exc()) + continue for strategy in self.strategies: strategy.on_compression_end(context) return context.eval_graph diff --git a/python/paddle/fluid/contrib/slim/core/config.py b/python/paddle/fluid/contrib/slim/core/config.py index 9bb395aee95b5236850ca51096ed870ab1d27b62..9b08a0324a58fad543ccecbee10b0499ea53dd88 100644 --- a/python/paddle/fluid/contrib/slim/core/config.py +++ b/python/paddle/fluid/contrib/slim/core/config.py @@ -20,11 +20,15 @@ from ..prune import * from ..quantization import * from .strategy import * from ..distillation import * +from ..searcher import * +from ..nas import * __all__ = ['ConfigFactory'] """This factory is used to create instances by loading and parsing configure file with yaml format. """ +PLUGINS = ['pruners', 'quantizers', 'quantizers', 'strategies', 'controllers'] + class ConfigFactory(object): def __init__(self, config): @@ -80,7 +84,7 @@ class ConfigFactory(object): assert self.version == int(key_values['version']) # parse pruners - if key == 'distillers' or key == 'pruners' or key == 'quantizers' or key == 'strategies': + if key in PLUGINS: instances = key_values[key] for name in instances: self._new_instance(name, instances[name]) @@ -91,8 +95,12 @@ class ConfigFactory(object): if 'init_model' in key_values[key]: self.compressor['init_model'] = key_values[key][ 'init_model'] - self.compressor['checkpoint_path'] = key_values[key][ - 'checkpoint_path'] + if 'checkpoint_path' in key_values[key]: + self.compressor['checkpoint_path'] = key_values[key][ + 'checkpoint_path'] + if 'eval_epoch' in key_values[key]: + self.compressor['eval_epoch'] = key_values[key][ + 'eval_epoch'] if 'strategies' in key_values[key]: for name in key_values[key]['strategies']: strategy = self.instance(name) diff --git a/python/paddle/fluid/contrib/slim/distillation/distillation_strategy.py b/python/paddle/fluid/contrib/slim/distillation/distillation_strategy.py index d8e08c3ebef50c9808ed818dcf35443dc25f850e..42389079f8de254699b880b7a48b0f30d435c7fc 100644 --- a/python/paddle/fluid/contrib/slim/distillation/distillation_strategy.py +++ b/python/paddle/fluid/contrib/slim/distillation/distillation_strategy.py @@ -14,14 +14,14 @@ from ..core.strategy import Strategy from ....framework import Program, Variable, program_guard +from ....log_helper import get_logger from .... import Executor import logging __all__ = ['DistillationStrategy'] -logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s') -_logger = logging.getLogger(__name__) -_logger.setLevel(logging.INFO) +_logger = get_logger( + __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') class DistillationStrategy(Strategy): diff --git a/python/paddle/fluid/contrib/slim/graph/executor.py b/python/paddle/fluid/contrib/slim/graph/executor.py index 70438a90eb790e7ca5d00be0bc09efc6c00cafe4..041ccbb3a315ccd22a0da26401c15fb6e3800859 100644 --- a/python/paddle/fluid/contrib/slim/graph/executor.py +++ b/python/paddle/fluid/contrib/slim/graph/executor.py @@ -41,6 +41,7 @@ class SlimGraphExecutor(object): results(list): A list of result with the same order indicated by graph.out_nodes. """ assert isinstance(graph, GraphWrapper) + feed = None if data is not None: feeder = DataFeeder( feed_list=graph.in_nodes.values(), diff --git a/python/paddle/fluid/contrib/slim/graph/graph_wrapper.py b/python/paddle/fluid/contrib/slim/graph/graph_wrapper.py index e7f5f0d6a2185521549abe7af7b6be2b0b7d90fb..b01c98aab9dae3296e19bf4108701e341d1f8ad9 100644 --- a/python/paddle/fluid/contrib/slim/graph/graph_wrapper.py +++ b/python/paddle/fluid/contrib/slim/graph/graph_wrapper.py @@ -209,6 +209,7 @@ class GraphWrapper(object): if var.persistable: self.persistables[var.name] = var self.compiled_graph = None + in_nodes = [] if in_nodes is None else in_nodes self.in_nodes = OrderedDict(in_nodes) self.out_nodes = OrderedDict(out_nodes) self._attrs = OrderedDict() @@ -241,7 +242,7 @@ class GraphWrapper(object): """ return var._var.persistable - def compile(self, for_parallel=True, for_test=False): + def compile(self, for_parallel=True, for_test=False, mem_opt=False): """ Compile the program in this wrapper to framework.CompiledProgram for next running. This function must be called if the program is modified. @@ -257,8 +258,9 @@ class GraphWrapper(object): if for_parallel: # disable memory optimize for stable training build_strategy = compiler.BuildStrategy() - build_strategy.enable_inplace = False - build_strategy.memory_optimize = False + build_strategy.enable_inplace = mem_opt + build_strategy.memory_optimize = mem_opt + # build_strategy.async_mode = False self.compiled_graph = compiler.CompiledProgram( target).with_data_parallel( loss_name=loss, build_strategy=build_strategy) @@ -475,8 +477,12 @@ class GraphWrapper(object): for var in self.program.list_vars(): if var.persistable and var.name not in self.persistables: self.persistables[var.name] = var + persistables = [] + for var in self.persistables: + if 'reader' not in var and 'double_buffer' not in var: + persistables.append(self.persistables[var]) - io.save_vars(exe.exe, path, vars=self.persistables.values()) + io.save_vars(exe.exe, path, vars=persistables) def load_persistables(self, path, exe): """ @@ -489,8 +495,11 @@ class GraphWrapper(object): def if_exist(var): return os.path.exists(os.path.join(path, var.name)) - io.load_vars( - exe.exe, path, vars=self.persistables.values(), predicate=if_exist) + persistables = [] + for var in self.persistables: + if 'reader' not in var and 'double_buffer' not in var: + persistables.append(self.persistables[var]) + io.load_vars(exe.exe, path, vars=persistables, predicate=if_exist) def update_param_shape(self, scope): """ diff --git a/python/paddle/fluid/contrib/slim/prune/__init__.py b/python/paddle/fluid/contrib/slim/prune/__init__.py index 764a45bb130a9993015858f1cbdbc9f3b864bd5e..ae487a21e341297dedb82cf275cc41badb9b2621 100644 --- a/python/paddle/fluid/contrib/slim/prune/__init__.py +++ b/python/paddle/fluid/contrib/slim/prune/__init__.py @@ -16,6 +16,9 @@ from . import pruner from .pruner import * from . import prune_strategy from .prune_strategy import * +from . import auto_prune_strategy +from .auto_prune_strategy import * __all__ = pruner.__all__ __all__ += prune_strategy.__all__ +__all__ += auto_prune_strategy.__all__ diff --git a/python/paddle/fluid/contrib/slim/prune/prune_strategy.py b/python/paddle/fluid/contrib/slim/prune/prune_strategy.py index 7a25c3a61e0815a20fa9b0477a6c69a4f8d2a066..6f430bc9e2fee375c813aeac1e05045b3b42afa4 100644 --- a/python/paddle/fluid/contrib/slim/prune/prune_strategy.py +++ b/python/paddle/fluid/contrib/slim/prune/prune_strategy.py @@ -15,6 +15,7 @@ from ..core.strategy import Strategy from ..graph import VarWrapper, OpWrapper, GraphWrapper from ....framework import Program, program_guard, Parameter +from ....log_helper import get_logger from .... import layers import prettytable as pt import numpy as np @@ -26,11 +27,10 @@ import pickle import logging import sys -__all__ = ['SensitivePruneStrategy', 'UniformPruneStrategy'] +__all__ = ['SensitivePruneStrategy', 'UniformPruneStrategy', 'PruneStrategy'] -logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s') -_logger = logging.getLogger(__name__) -_logger.setLevel(logging.INFO) +_logger = get_logger( + __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') class PruneStrategy(Strategy): @@ -61,8 +61,6 @@ class PruneStrategy(Strategy): self.metric_name = metric_name self.pruned_params = pruned_params self.pruned_list = [] - self.backup = {} - self.param_shape_backup = {} def _eval_graph(self, context, sampled_rate=None, cached_id=0): """ @@ -82,7 +80,9 @@ class PruneStrategy(Strategy): ratio, place, lazy=False, - only_graph=False): + only_graph=False, + param_shape_backup=None, + param_backup=None): """ Pruning filters by given ratio. Args: @@ -103,16 +103,16 @@ class PruneStrategy(Strategy): for param in params: assert isinstance(param, VarWrapper) param_t = scope.find_var(param.name()).get_tensor() - if lazy: - self.backup[param.name()] = copy.deepcopy(np.array(param_t)) + if param_backup is not None and (param.name() not in param_backup): + param_backup[param.name()] = copy.deepcopy(np.array(param_t)) pruned_param = self.pruner.prune_tensor( np.array(param_t), pruned_idx, pruned_axis=0, lazy=lazy) if not only_graph: param_t.set(pruned_param, place) ori_shape = param.shape() - if param.name() not in self.param_shape_backup: - self.param_shape_backup[param.name()] = copy.deepcopy( - param.shape()) + if param_shape_backup is not None and ( + param.name() not in param_shape_backup): + param_shape_backup[param.name()] = copy.deepcopy(param.shape()) new_shape = list(param.shape()) new_shape[0] = pruned_param.shape[0] param.set_shape(new_shape) @@ -120,7 +120,8 @@ class PruneStrategy(Strategy): '|----------------------------------------+----+------------------------------+------------------------------|' ) _logger.debug('|{:^40}|{:^4}|{:^30}|{:^30}|'.format( - str(param.name()), str(0), str(ori_shape), str(param.shape()))) + str(param.name()), + str(ratio), str(ori_shape), str(param.shape()))) self.pruned_list[0].append(param.name()) return pruned_idx @@ -131,7 +132,9 @@ class PruneStrategy(Strategy): pruned_axis, place, lazy=False, - only_graph=False): + only_graph=False, + param_shape_backup=None, + param_backup=None): """ Pruning parameters in given axis. Args: @@ -150,16 +153,17 @@ class PruneStrategy(Strategy): for param in params: assert isinstance(param, VarWrapper) param_t = scope.find_var(param.name()).get_tensor() - if lazy: - self.backup[param.name()] = copy.deepcopy(np.array(param_t)) + if param_backup is not None and (param.name() not in param_backup): + param_backup[param.name()] = copy.deepcopy(np.array(param_t)) pruned_param = self.pruner.prune_tensor( np.array(param_t), pruned_idx, pruned_axis, lazy=lazy) if not only_graph: param_t.set(pruned_param, place) ori_shape = param.shape() - if param.name() not in self.param_shape_backup: - self.param_shape_backup[param.name()] = copy.deepcopy( - param.shape()) + + if param_shape_backup is not None and ( + param.name() not in param_shape_backup): + param_shape_backup[param.name()] = copy.deepcopy(param.shape()) new_shape = list(param.shape()) new_shape[pruned_axis] = pruned_param.shape[pruned_axis] param.set_shape(new_shape) @@ -251,7 +255,9 @@ class PruneStrategy(Strategy): ratio=None, pruned_idxs=None, lazy=False, - only_graph=False): + only_graph=False, + param_backup=None, + param_shape_backup=None): """ Pruning all the parameters affected by the pruning of given parameter. Args: @@ -284,7 +290,9 @@ class PruneStrategy(Strategy): pruned_axis=0, place=place, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) else: pruned_idxs = self._prune_filters_by_ratio( @@ -292,7 +300,9 @@ class PruneStrategy(Strategy): ratio, place, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) corrected_idxs = pruned_idxs[:] for idx, op in enumerate(related_ops): @@ -307,7 +317,9 @@ class PruneStrategy(Strategy): pruned_axis=1, place=place, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) if op.type() == "depthwise_conv2d": for in_var in op.all_inputs(): if graph.is_parameter(in_var): @@ -319,7 +331,9 @@ class PruneStrategy(Strategy): pruned_axis=0, place=place, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) elif op.type() == "elementwise_add": # pruning bias for in_var in op.all_inputs(): @@ -332,7 +346,9 @@ class PruneStrategy(Strategy): pruned_axis=0, place=place, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) elif op.type() == "mul": # pruning fc layer fc_input = None fc_param = None @@ -354,7 +370,9 @@ class PruneStrategy(Strategy): pruned_axis=0, place=place, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) elif op.type() == "concat": concat_inputs = op.all_inputs() @@ -378,28 +396,36 @@ class PruneStrategy(Strategy): pruned_axis=0, place=place, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) self._prune_parameter_by_idx( scope, [variance] + self._get_accumulator(graph, variance), corrected_idxs, pruned_axis=0, place=place, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) self._prune_parameter_by_idx( scope, [alpha] + self._get_accumulator(graph, alpha), corrected_idxs, pruned_axis=0, place=place, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) self._prune_parameter_by_idx( scope, [beta] + self._get_accumulator(graph, beta), corrected_idxs, pruned_axis=0, place=place, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) def _prune_parameters(self, graph, @@ -408,7 +434,9 @@ class PruneStrategy(Strategy): ratios, place, lazy=False, - only_graph=False): + only_graph=False, + param_backup=None, + param_shape_backup=None): """ Pruning the given parameters. Args: @@ -444,7 +472,9 @@ class PruneStrategy(Strategy): place, ratio=ratio, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) ops = param.outputs() for op in ops: if op.type() == 'conv2d': @@ -458,7 +488,9 @@ class PruneStrategy(Strategy): place, ratio=ratio, lazy=lazy, - only_graph=only_graph) + only_graph=only_graph, + param_backup=param_backup, + param_shape_backup=param_shape_backup) _logger.debug( '|----------------------------------------+----+------------------------------+------------------------------|' ) @@ -575,23 +607,24 @@ class UniformPruneStrategy(PruneStrategy): _logger.debug( '-----------Try pruning ratio: {:.2f}-----------'.format(ratio)) ratios = [ratio] * len(pruned_params) + param_shape_backup = {} self._prune_parameters( context.eval_graph, context.scope, pruned_params, ratios, context.place, - only_graph=True) + only_graph=True, + param_shape_backup=param_shape_backup) pruned_flops = 1 - (float(context.eval_graph.flops()) / flops) pruned_size = 1 - (float(context.eval_graph.numel_params()) / model_size) _logger.debug('Pruned flops: {:.2f}'.format(pruned_flops)) _logger.debug('Pruned model size: {:.2f}'.format(pruned_size)) - for param in self.param_shape_backup.keys(): - context.eval_graph.var(param).set_shape(self.param_shape_backup[ + for param in param_shape_backup.keys(): + context.eval_graph.var(param).set_shape(param_shape_backup[ param]) - self.param_shape_backup = {} if abs(pruned_flops - self.target_ratio) < 1e-2: break @@ -672,8 +705,6 @@ class SensitivePruneStrategy(PruneStrategy): self.pruned_list = [] self.sensitivities = sensitivities self.sensitivities_file = sensitivities_file - self.backup = {} - self.param_shape_backup = {} self.num_steps = num_steps self.eval_rate = eval_rate self.pruning_step = 1 - pow((1 - target_ratio), 1.0 / self.num_steps) @@ -728,8 +759,6 @@ class SensitivePruneStrategy(PruneStrategy): Computing the sensitivities of all parameters. """ _logger.info("calling _compute_sensitivities.") - self.param_shape_backup = {} - self.backup = {} cached_id = np.random.randint(1000) if self.start_epoch == context.epoch_id: sensitivities_file = self.sensitivities_file @@ -761,12 +790,15 @@ class SensitivePruneStrategy(PruneStrategy): if metric is None: metric = self._eval_graph(context, self.eval_rate, cached_id) + + param_backup = {} # prune parameter by ratio self._prune_parameters( context.eval_graph, context.scope, [param], [ratio], context.place, - lazy=True) + lazy=True, + param_backup=param_backup) self.pruned_list[0] # get accuracy after pruning and update self.sensitivities pruned_metric = self._eval_graph(context, self.eval_rate, @@ -787,12 +819,11 @@ class SensitivePruneStrategy(PruneStrategy): self._save_sensitivities(sensitivities, sensitivities_file) # restore pruned parameters - for param_name in self.backup.keys(): + for param_name in param_backup.keys(): param_t = context.scope.find_var(param_name).get_tensor() - param_t.set(self.backup[param_name], context.place) + param_t.set(self.param_backup[param_name], context.place) # pruned_metric = self._eval_graph(context) - self.backup = {} ratio += self.delta_rate return sensitivities @@ -803,8 +834,6 @@ class SensitivePruneStrategy(PruneStrategy): """ _logger.info('_get_best_ratios for pruning ratie: {}'.format( target_ratio)) - self.param_shape_backup = {} - self.backup = {} def func(params, x): a, b, c, d = params @@ -854,23 +883,24 @@ class SensitivePruneStrategy(PruneStrategy): _logger.info('Pruned ratios={}'.format( [round(ratio, 3) for ratio in ratios])) # step 2.2: Pruning by current ratios + param_shape_backup = {} self._prune_parameters( context.eval_graph, context.scope, sensitivities.keys(), ratios, context.place, - only_graph=True) + only_graph=True, + param_shape_backup=param_shape_backup) pruned_flops = 1 - (float(context.eval_graph.flops()) / flops) pruned_size = 1 - (float(context.eval_graph.numel_params()) / model_size) _logger.info('Pruned flops: {:.4f}'.format(pruned_flops)) _logger.info('Pruned model size: {:.4f}'.format(pruned_size)) - for param in self.param_shape_backup.keys(): - context.eval_graph.var(param).set_shape(self.param_shape_backup[ + for param in param_shape_backup.keys(): + context.eval_graph.var(param).set_shape(param_shape_backup[ param]) - self.param_shape_backup = {} # step 2.3: Check whether current ratios is enough if abs(pruned_flops - target_ratio) < 0.015: @@ -902,9 +932,6 @@ class SensitivePruneStrategy(PruneStrategy): self._prune_parameters(context.optimize_graph, context.scope, params, ratios, context.place) - self.param_shape_backup = {} - self.backup = {} - model_size = context.eval_graph.numel_params() flops = context.eval_graph.flops() _logger.debug('################################') diff --git a/python/paddle/fluid/contrib/slim/quantization/__init__.py b/python/paddle/fluid/contrib/slim/quantization/__init__.py index 1c51aa15373779b06273296a27d913c070079f41..659265895a594862c3e32c6360f7ddabf53e3b64 100644 --- a/python/paddle/fluid/contrib/slim/quantization/__init__.py +++ b/python/paddle/fluid/contrib/slim/quantization/__init__.py @@ -18,5 +18,11 @@ from . import quantization_pass from .quantization_pass import * from . import quantization_strategy from .quantization_strategy import * +from . import mkldnn_post_training_strategy +from .mkldnn_post_training_strategy import * +from . import quantization_mkldnn_pass +from .quantization_mkldnn_pass import * __all__ = quantization_pass.__all__ + quantization_strategy.__all__ +__all__ += mkldnn_post_training_strategy.__all__ +__all__ += quantization_mkldnn_pass.__all__ diff --git a/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py b/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py index 0d989903a9aea018913e3ee30e2b80f9341f77c0..1ea2f080c64021915b80efd746dcc6a1e8b6f7fb 100644 --- a/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py +++ b/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py @@ -22,7 +22,8 @@ from .... import unique_name __all__ = [ 'QuantizationTransformPass', 'QuantizationFreezePass', 'ConvertToInt8Pass', - 'TransformForMobilePass', 'ScaleForTrainingPass', 'ScaleForInferencePass' + 'TransformForMobilePass', 'ScaleForTrainingPass', 'ScaleForInferencePass', + 'AddQuantDequantPass' ] @@ -994,6 +995,8 @@ class ScaleForTrainingPass(object): Args: graph(IrGraph): the target graph. """ + assert isinstance(graph, + IrGraph), 'graph must be the instance of IrGraph.' self._is_test = graph.is_test() ops = graph.all_op_nodes() for op_node in ops: @@ -1099,6 +1102,8 @@ class ScaleForInferencePass(object): Args: graph(IrGraph): the target graph. """ + assert isinstance(graph, + IrGraph), 'graph must be the instance of IrGraph.' ops = graph.all_op_nodes() for op_node in ops: name = op_node.name() @@ -1117,3 +1122,137 @@ class ScaleForInferencePass(object): Return the scale name for the var named `var_name`. """ return "%s@scale" % (var_name) + + +class AddQuantDequantPass(object): + def __init__(self, scope=None, place=None, moving_rate=0.9, quant_bits=8): + """ + This pass is used to add quant_dequant op for some ops, such as the + `elementwise_add` op. + """ + self._scope = scope + self._place = place + self._moving_rate = moving_rate + self._quant_bits = quant_bits + self._is_test = None + self._target_ops = ["elementwise_add", "pool2d"] + + def apply(self, graph): + """ + Add quant_dequant before some ops, such as the `elementwise_add` op. This + is required by TensorRT. + Args: + graph(IrGraph): the target graph. + """ + assert isinstance(graph, + IrGraph), 'graph must be the instance of IrGraph.' + self._is_test = graph.is_test() + ops = graph.all_op_nodes() + for op_node in ops: + name = op_node.name() + if name in self._target_ops: + in_nodes_all_not_persistable = True + for input_name in op_node.input_arg_names(): + in_node = graph._find_node_by_name(op_node.inputs, + input_name) + in_nodes_all_not_persistable = ( + in_nodes_all_not_persistable and + not in_node.persistable()) + if not in_nodes_all_not_persistable: + continue + input_names = op_node.input_arg_names() + for input_name in input_names: + in_node = graph._find_node_by_name(op_node.inputs, + input_name) + quant_var_node, scale_var_node = self._inser_quant_dequant_moving_average_abs_max_op( + graph, in_node, self._quant_bits) + graph.update_input_link(in_node, quant_var_node, op_node) + graph.resolve_hazard() + return graph + + def _inser_quant_dequant_moving_average_abs_max_op(self, graph, var_node, + quant_bits): + """Insert fake_quantize_dequantize_moving_average_abs_max op. + """ + quant_var_node = graph.create_var_node( + name="{}.quant_dequant".format(var_node.name()), + var_type=var_node.type(), + shape=var_node.shape(), + var_dtype=var_node.dtype()) + scale_in_node = graph.create_persistable_node( + name="{}.quant_dequant.scale".format(var_node.name()), + var_type=core.VarDesc.VarType.LOD_TENSOR, + shape=[1], + var_dtype=var_node.dtype()) + data_type = 'float64' if var_node.dtype( + ) == core.VarDesc.VarType.FP64 else 'float32' + _init_var_node( + scale_in_node, + np.array( + [0.001], dtype=data_type), + self._scope, + self._place) + + scale_out_node = graph.create_var_node_from_desc(scale_in_node.var()) + ins = {'X': var_node, 'InScale': scale_in_node} + outs = {'Out': quant_var_node, 'OutScale': scale_out_node} + if not self._is_test: + state_in_node = graph.create_persistable_node( + name=unique_name.generate('quant_dequant.state'), + var_type=core.VarDesc.VarType.LOD_TENSOR, + var_dtype=var_node.dtype(), + shape=[1]) + data_type = 'float64' if var_node.dtype( + ) == core.VarDesc.VarType.FP64 else 'float32' + _init_var_node( + state_in_node, + np.ones( + [1], dtype=data_type), + self._scope, + self._place) + accum_in_node = graph.create_persistable_node( + name=unique_name.generate('quant_dequant.accum'), + var_type=core.VarDesc.VarType.LOD_TENSOR, + var_dtype=var_node.dtype(), + shape=[1]) + _init_var_node( + accum_in_node, + np.ones( + [1], dtype=data_type), + self._scope, + self._place) + state_out_node = graph.create_var_node_from_desc(state_in_node.var( + )) + accum_out_node = graph.create_var_node_from_desc(accum_in_node.var( + )) + + ins['InState'] = state_in_node + ins['InAccum'] = accum_in_node + outs['OutState'] = state_out_node + outs['OutAccum'] = accum_out_node + + attrs = { + 'bit_length': quant_bits, + 'moving_rate': self._moving_rate, + 'is_test': self._is_test, + 'op_role': core.op_proto_and_checker_maker.OpRole.Forward + } + + quant_op_node = graph.create_op_node( + op_type='fake_quantize_dequantize_moving_average_abs_max', + attrs=attrs, + inputs=ins, + outputs=outs) + + graph.link_to(var_node, quant_op_node) + graph.link_to(scale_in_node, quant_op_node) + graph.link_to(quant_op_node, quant_var_node) + graph.link_to(quant_op_node, scale_out_node) + + if not self._is_test: + graph.link_to(state_in_node, quant_op_node) + graph.link_to(accum_in_node, quant_op_node) + graph.link_to(quant_op_node, state_out_node) + graph.link_to(quant_op_node, accum_out_node) + + return quant_var_node, scale_out_node diff --git a/python/paddle/fluid/contrib/slim/quantization/quantization_strategy.py b/python/paddle/fluid/contrib/slim/quantization/quantization_strategy.py index 12c1ce98992c32caaa300045c4adc918dd88f427..c3d977f708f443951e4d05809531161a9257e7ae 100644 --- a/python/paddle/fluid/contrib/slim/quantization/quantization_strategy.py +++ b/python/paddle/fluid/contrib/slim/quantization/quantization_strategy.py @@ -21,14 +21,14 @@ from .... import core from ....compiler import CompiledProgram from ....compiler import BuildStrategy from ....framework import IrGraph, Variable, Program +from ....log_helper import get_logger from ..core.strategy import Strategy from .quantization_pass import * __all__ = ['QuantizationStrategy'] -logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s') -_logger = logging.getLogger(__name__) -_logger.setLevel(logging.INFO) +_logger = get_logger( + __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') class QuantizationStrategy(Strategy): diff --git a/python/paddle/fluid/contrib/slim/tests/CMakeLists.txt b/python/paddle/fluid/contrib/slim/tests/CMakeLists.txt index 848f063f67716f6d348ba21d697ad7373783ee22..c59df49f6260c25ebdb0290b30d3bdb78b98a3c6 100644 --- a/python/paddle/fluid/contrib/slim/tests/CMakeLists.txt +++ b/python/paddle/fluid/contrib/slim/tests/CMakeLists.txt @@ -1,11 +1,147 @@ file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py") string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}") +function(inference_analysis_python_api_int8_test target model_dir data_dir filename) + py_test(${target} SRCS ${filename} + ENVS CPU_NUM_THREADS=${CPU_NUM_THREADS_ON_CI} + ARGS --infer_model ${model_dir}/model + --infer_data ${data_dir}/data.bin + --int8_model_save_path int8_models/${target} + --warmup_batch_size 100 + --batch_size 50) +endfunction() + +function(inference_qat_int8_test target model_dir data_dir test_script use_mkldnn) + py_test(${target} SRCS ${test_script} + ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI} + OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI} + FLAGS_use_mkldnn=${use_mkldnn} + ARGS --qat_model ${model_dir}/model + --infer_data ${data_dir}/data.bin + --batch_size 25 + --batch_num 2 + --acc_diff_threshold 0.1) +endfunction() + # NOTE: TODOOOOOOOOOOO # temporarily disable test_distillation_strategy since it always failed on a specified machine with 4 GPUs # Need to figure out the root cause and then add it back list(REMOVE_ITEM TEST_OPS test_distillation_strategy) +if(WIN32) + list(REMOVE_ITEM TEST_OPS test_light_nas) +endif() + +# int8 image classification python api test +if(LINUX AND WITH_MKLDNN) + set(INT8_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2") + set(MKLDNN_INT8_TEST_FILE "test_mkldnn_int8_quantization_strategy.py") + + # googlenet int8 + set(INT8_GOOGLENET_MODEL_DIR "${INT8_DATA_DIR}/googlenet") + inference_analysis_python_api_int8_test(test_slim_int8_googlenet ${INT8_GOOGLENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE}) + + # mobilenet int8 + set(INT8_MOBILENET_MODEL_DIR "${INT8_DATA_DIR}/mobilenetv1") + inference_analysis_python_api_int8_test(test_slim_int8_mobilenet ${INT8_MOBILENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE}) + + # temporarily adding WITH_SLIM_MKLDNN_FULL_TEST FLAG for QA testing the following UTs locally, + # since the following UTs cost too much time on CI test. + if (WITH_SLIM_MKLDNN_FULL_TEST) + # resnet50 int8 + set(INT8_RESNET50_MODEL_DIR "${INT8_DATA_DIR}/resnet50") + inference_analysis_python_api_int8_test(test_slim_int8_resnet50 ${INT8_RESNET50_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE}) + + # mobilenetv2 int8 + set(INT8_MOBILENETV2_MODEL_DIR "${INT8_DATA_DIR}/mobilenetv2") + inference_analysis_python_api_int8_test(test_slim_int8_mobilenetv2 ${INT8_MOBILENETV2_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE}) + + # resnet101 int8 + set(INT8_RESNET101_MODEL_DIR "${INT8_DATA_DIR}/resnet101") + inference_analysis_python_api_int8_test(test_slim_int8_resnet101 ${INT8_RESNET101_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE}) + + # vgg16 int8 + set(INT8_VGG16_MODEL_DIR "${INT8_DATA_DIR}/vgg16") + inference_analysis_python_api_int8_test(test_slim_int8_vgg16 ${INT8_VGG16_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE}) + + # vgg19 int8 + set(INT8_VGG19_MODEL_DIR "${INT8_DATA_DIR}/vgg19") + inference_analysis_python_api_int8_test(test_slim_int8_vgg19 ${INT8_VGG19_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE}) + endif() +endif() + +# Since test_mkldnn_int8_quantization_strategy only supports testing on Linux +# with MKL-DNN, we remove it here for not repeating test, or not testing on other systems. +list(REMOVE_ITEM TEST_OPS test_mkldnn_int8_quantization_strategy) + +# QAT FP32 & INT8 comparison python api tests +if(LINUX AND WITH_MKLDNN) + set(DATASET_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2") + set(QAT_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2") + set(QAT_MODELS_BASE_URL "${INFERENCE_URL}/int8/QAT_models") + set(MKLDNN_QAT_TEST_FILE "qat_int8_comparison.py") + set(MKLDNN_QAT_TEST_FILE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/${MKLDNN_QAT_TEST_FILE}") + + # ImageNet small dataset + # May be already downloaded for INT8v2 unit tests + if (NOT EXISTS ${DATASET_DIR}) + inference_download_and_uncompress(${DATASET_DIR} "${INFERENCE_URL}/int8" "imagenet_val_100_tail.tar.gz") + endif() + + # QAT ResNet50 + set(QAT_RESNET50_MODEL_DIR "${QAT_DATA_DIR}/ResNet50_QAT") + if (NOT EXISTS ${QAT_RESNET50_MODEL_DIR}) + inference_download_and_uncompress(${QAT_RESNET50_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "ResNet50_qat_model.tar.gz" ) + endif() + inference_qat_int8_test(test_qat_int8_resnet50_mkldnn ${QAT_RESNET50_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) + + # QAT ResNet101 + set(QAT_RESNET101_MODEL_DIR "${QAT_DATA_DIR}/ResNet101_QAT") + if (NOT EXISTS ${QAT_RESNET101_MODEL_DIR}) + inference_download_and_uncompress(${QAT_RESNET101_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "ResNet101_qat_model.tar.gz" ) + endif() + inference_qat_int8_test(test_qat_int8_resnet101_mkldnn ${QAT_RESNET101_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) + + # QAT GoogleNet + set(QAT_GOOGLENET_MODEL_DIR "${QAT_DATA_DIR}/GoogleNet_QAT") + if (NOT EXISTS ${QAT_GOOGLENET_MODEL_DIR}) + inference_download_and_uncompress(${QAT_GOOGLENET_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "GoogleNet_qat_model.tar.gz" ) + endif() + inference_qat_int8_test(test_qat_int8_googlenet_mkldnn ${QAT_GOOGLENET_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) + + # QAT MobileNetV1 + set(QAT_MOBILENETV1_MODEL_DIR "${QAT_DATA_DIR}/MobileNetV1_QAT") + if (NOT EXISTS ${QAT_MOBILENETV1_MODEL_DIR}) + inference_download_and_uncompress(${QAT_MOBILENETV1_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "MobileNetV1_qat_model.tar.gz" ) + endif() + inference_qat_int8_test(test_qat_int8_mobilenetv1_mkldnn ${QAT_MOBILENETV1_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) + + # QAT MobileNetV2 + set(QAT_MOBILENETV2_MODEL_DIR "${QAT_DATA_DIR}/MobileNetV2_QAT") + if (NOT EXISTS ${QAT_MOBILENETV2_MODEL_DIR}) + inference_download_and_uncompress(${QAT_MOBILENETV2_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "MobileNetV2_qat_model.tar.gz" ) + endif() + inference_qat_int8_test(test_qat_int8_mobilenetv2_mkldnn ${QAT_MOBILENETV2_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) + + # QAT VGG16 + set(QAT_VGG16_MODEL_DIR "${QAT_DATA_DIR}/VGG16_QAT") + if (NOT EXISTS ${QAT_VGG16_MODEL_DIR}) + inference_download_and_uncompress(${QAT_VGG16_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "VGG16_qat_model.tar.gz" ) + endif() + inference_qat_int8_test(test_qat_int8_vgg16_mkldnn ${QAT_VGG16_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) + + # QAT VGG19 + set(QAT_VGG19_MODEL_DIR "${QAT_DATA_DIR}/VGG19_QAT") + if (NOT EXISTS ${QAT_VGG19_MODEL_DIR}) + inference_download_and_uncompress(${QAT_VGG19_MODEL_DIR} "${QAT_MODELS_BASE_URL}" "VGG19_qat_model.tar.gz" ) + endif() + inference_qat_int8_test(test_qat_int8_vgg19_mkldnn ${QAT_VGG19_MODEL_DIR} ${DATASET_DIR} ${MKLDNN_QAT_TEST_FILE_PATH} true) +endif() + +# Since the test for QAT FP32 & INT8 comparison supports only testing on Linux +# with MKL-DNN, we remove it here to not test it on other systems. +list(REMOVE_ITEM TEST_OPS qat_int8_comparison.py) + foreach(src ${TEST_OPS}) py_test(${src} SRCS ${src}.py) endforeach() diff --git a/python/paddle/fluid/contrib/slim/tests/test_graph_wrapper.py b/python/paddle/fluid/contrib/slim/tests/test_graph_wrapper.py index 0ab8052d7ab16743bb6589dbb44203e70fa907d0..69080cf50ecaf8a290984f2792ec697a1edf3234 100644 --- a/python/paddle/fluid/contrib/slim/tests/test_graph_wrapper.py +++ b/python/paddle/fluid/contrib/slim/tests/test_graph_wrapper.py @@ -19,6 +19,8 @@ import six import numpy as np from paddle.fluid.contrib.slim.graph import GraphWrapper from paddle.fluid import core +import os +os.environ['CPU_NUM'] = str(4) def residual_block(num): diff --git a/python/paddle/fluid/contrib/slim/tests/test_quantization_scale_pass.py b/python/paddle/fluid/contrib/slim/tests/test_quantization_scale_pass.py index 1ed41da0f842b5eac8fd622a96a2fbd68adf98ae..0739c9c1f7b9b7250e9743d496df5d29fb0d6ea9 100644 --- a/python/paddle/fluid/contrib/slim/tests/test_quantization_scale_pass.py +++ b/python/paddle/fluid/contrib/slim/tests/test_quantization_scale_pass.py @@ -24,6 +24,7 @@ from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass from paddle.fluid.contrib.slim.quantization import ScaleForTrainingPass from paddle.fluid.contrib.slim.quantization import ScaleForInferencePass +from paddle.fluid.contrib.slim.quantization import AddQuantDequantPass from paddle.fluid import core os.environ["CUDA_VISIBLE_DEVICES"] = "0" @@ -98,6 +99,7 @@ class TestQuantizationScalePass(unittest.TestCase): scope = fluid.Scope() with fluid.scope_guard(scope): exe.run(startup) + transform_pass = QuantizationTransformPass( scope=scope, place=place, @@ -105,8 +107,14 @@ class TestQuantizationScalePass(unittest.TestCase): weight_quantize_type=weight_quant_type) transform_pass.apply(main_graph) transform_pass.apply(test_graph) + + add_quant_dequant_pass = AddQuantDequantPass(scope=scope, place=place) + add_quant_dequant_pass.apply(main_graph) + add_quant_dequant_pass.apply(test_graph) + scale_training_pass = ScaleForTrainingPass(scope=scope, place=place) scale_training_pass.apply(main_graph) + dev_name = '_gpu' if use_cuda else '_cpu' if not for_ci: marked_nodes = set() diff --git a/python/paddle/fluid/contrib/tests/test_calibration_mobilenetv1.py b/python/paddle/fluid/contrib/tests/test_calibration_mobilenetv1.py index 4eb397e55b783d5ce23eb4fb3b56fa28c1743078..214d6c7557f9d5194e1913610fd7f7d784c61fed 100644 --- a/python/paddle/fluid/contrib/tests/test_calibration_mobilenetv1.py +++ b/python/paddle/fluid/contrib/tests/test_calibration_mobilenetv1.py @@ -30,16 +30,16 @@ class TestCalibrationForMobilenetv1(TestCalibration): def test_calibration(self): self.download_model() - print("Start FP32 inference for {0} on {1} images ...").format( - self.model, self.infer_iterations * self.batch_size) + print("Start FP32 inference for {0} on {1} images ...".format( + self.model, self.infer_iterations * self.batch_size)) (fp32_throughput, fp32_latency, fp32_acc1) = self.run_program(self.model_cache_folder + "/model") - print("Start INT8 calibration for {0} on {1} images ...").format( - self.model, self.sample_iterations * self.batch_size) + print("Start INT8 calibration for {0} on {1} images ...".format( + self.model, self.sample_iterations * self.batch_size)) self.run_program( self.model_cache_folder + "/model", True, algo=self.algo) - print("Start INT8 inference for {0} on {1} images ...").format( - self.model, self.infer_iterations * self.batch_size) + print("Start INT8 inference for {0} on {1} images ...".format( + self.model, self.infer_iterations * self.batch_size)) (int8_throughput, int8_latency, int8_acc1) = self.run_program(self.int8_model) delta_value = fp32_acc1 - int8_acc1 diff --git a/python/paddle/fluid/contrib/tests/test_calibration_resnet50.py b/python/paddle/fluid/contrib/tests/test_calibration_resnet50.py index 0bbaa21a7111a693d74b46c0657f009638bc1b1a..a5286e5b0a6858a795bb221ad02f9d466eb7d751 100644 --- a/python/paddle/fluid/contrib/tests/test_calibration_resnet50.py +++ b/python/paddle/fluid/contrib/tests/test_calibration_resnet50.py @@ -193,7 +193,7 @@ class TestCalibration(unittest.TestCase): file_name = data_urls[0].split('/')[-1] zip_path = os.path.join(self.cache_folder, file_name) - print('Data is downloaded at {0}').format(zip_path) + print('Data is downloaded at {0}'.format(zip_path)) self.cache_unzipping(data_cache_folder, zip_path) return data_cache_folder @@ -297,16 +297,16 @@ class TestCalibrationForResnet50(TestCalibration): def test_calibration(self): self.download_model() - print("Start FP32 inference for {0} on {1} images ...").format( - self.model, self.infer_iterations * self.batch_size) + print("Start FP32 inference for {0} on {1} images ...".format( + self.model, self.infer_iterations * self.batch_size)) (fp32_throughput, fp32_latency, fp32_acc1) = self.run_program(self.model_cache_folder + "/model") - print("Start INT8 calibration for {0} on {1} images ...").format( - self.model, self.sample_iterations * self.batch_size) + print("Start INT8 calibration for {0} on {1} images ...".format( + self.model, self.sample_iterations * self.batch_size)) self.run_program( self.model_cache_folder + "/model", True, algo=self.algo) - print("Start INT8 inference for {0} on {1} images ...").format( - self.model, self.infer_iterations * self.batch_size) + print("Start INT8 inference for {0} on {1} images ...".format( + self.model, self.infer_iterations * self.batch_size)) (int8_throughput, int8_latency, int8_acc1) = self.run_program(self.int8_model) delta_value = fp32_acc1 - int8_acc1 diff --git a/python/paddle/fluid/contrib/tests/test_image_classification_fp16.py b/python/paddle/fluid/contrib/tests/test_image_classification_fp16.py index b7a14fa59b48a0304b72249e79609e87d827c4e8..a2e700803dcf3a2da5b7f1e15b68fb8b274a939a 100644 --- a/python/paddle/fluid/contrib/tests/test_image_classification_fp16.py +++ b/python/paddle/fluid/contrib/tests/test_image_classification_fp16.py @@ -132,10 +132,12 @@ def train(net_type, use_cuda, save_dirname, is_local): # Test program test_program = train_program.clone(for_test=True) - optimizer = fluid.optimizer.Adam(learning_rate=0.001) + optimizer = fluid.optimizer.Lamb(learning_rate=0.001) mp_optimizer = fluid.contrib.mixed_precision.decorate( - optimizer=optimizer, init_loss_scaling=8.0) + optimizer=optimizer, + init_loss_scaling=8.0, + use_dynamic_loss_scaling=True) scaled_loss, _, _ = mp_optimizer.minimize(avg_cost) diff --git a/python/paddle/fluid/contrib/utils/hdfs_utils.py b/python/paddle/fluid/contrib/utils/hdfs_utils.py index 35ddf97ff2361d8abd34b16761be99990fc3880d..1bfc966de88e5e816497eea0a7a0d0f2dd667355 100644 --- a/python/paddle/fluid/contrib/utils/hdfs_utils.py +++ b/python/paddle/fluid/contrib/utils/hdfs_utils.py @@ -24,12 +24,12 @@ import copy import errno import logging +from paddle.fluid.log_helper import get_logger __all__ = ["HDFSClient", "multi_download", "multi_upload"] -logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s') -_logger = logging.getLogger("hdfs_utils") -_logger.setLevel(logging.INFO) +_logger = get_logger( + __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') class HDFSClient(object): diff --git a/python/paddle/fluid/contrib/utils/lookup_table_utils.py b/python/paddle/fluid/contrib/utils/lookup_table_utils.py index a127f5b11b7ce681c09ef8d08281a2982e25e9eb..b15ee94f63512dcca91a8aab33d216db0fc24ed5 100644 --- a/python/paddle/fluid/contrib/utils/lookup_table_utils.py +++ b/python/paddle/fluid/contrib/utils/lookup_table_utils.py @@ -22,15 +22,17 @@ import paddle from paddle.fluid import core from paddle.fluid import io from paddle.fluid import Program +from paddle.fluid.log_helper import get_logger __all__ = [ "load_persistables_for_increment", "load_persistables_for_inference", "convert_dist_to_sparse_program" ] -logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s') -_logger = logging.getLogger("lookup_table_utils") -_logger.setLevel(logging.INFO) +_logger = get_logger( + 'lookup_table_utils', + logging.INFO, + fmt='%(asctime)s-%(levelname)s: %(message)s') model_filename = "__model__" lookup_table_dir = "__lookup_table__" diff --git a/python/paddle/fluid/data_feed_desc.py b/python/paddle/fluid/data_feed_desc.py index 80745aac830d1da46b62ab1bf246b1fa4895a7cc..5ed38f9999f079e3c6df1c1770a6dc1087bd0e93 100644 --- a/python/paddle/fluid/data_feed_desc.py +++ b/python/paddle/fluid/data_feed_desc.py @@ -24,28 +24,32 @@ class DataFeedDesc(object): currently only used for AsyncExecutor (See comments for class AsyncExecutor for a brief introduction) - DataFeedDesc shall be initialized from a valid protobuf message from disk: - >>> data_feed = fluid.DataFeedDesc('data.proto') + DataFeedDesc shall be initialized from a valid protobuf message from disk. See :code:`paddle/fluid/framework/data_feed.proto` for message definition. A typical message might look like: - >>> name: "MultiSlotDataFeed" - >>> batch_size: 2 - >>> multi_slot_desc { - >>> slots { - >>> name: "words" - >>> type: "uint64" - >>> is_dense: false - >>> is_used: true - >>> } - >>> slots { - >>> name: "label" - >>> type: "uint64" - >>> is_dense: false - >>> is_used: true - >>> } - >>> } + .. code-block:: python + + f = open("data.proto", "w") + print >> f, 'name: "MultiSlotDataFeed"' + print >> f, 'batch_size: 2' + print >> f, 'multi_slot_desc {' + print >> f, ' slots {' + print >> f, ' name: "words"' + print >> f, ' type: "uint64"' + print >> f, ' is_dense: false' + print >> f, ' is_used: true' + print >> f, ' }' + print >> f, ' slots {' + print >> f, ' name: "label"' + print >> f, ' type: "uint64"' + print >> f, ' is_dense: false' + print >> f, ' is_used: true' + print >> f, ' }' + print >> f, '}' + f.close() + data_feed = fluid.DataFeedDesc('data.proto') However, users usually shouldn't care about the message format; instead, they are encouragd to use :code:`Data Generator` as a tool to generate a @@ -54,16 +58,23 @@ class DataFeedDesc(object): DataFeedDesc can also be changed during runtime. Once you got familiar with what each field mean, you can modify it to better suit your need. E.g.: - >>> data_feed.set_batch_size(128) - >>> data_feed.set_dense_slots('wd') # The slot named 'wd' will be dense - >>> data_feed.set_use_slots('wd') # The slot named 'wd' will be used + + .. code-block:: python + + data_feed = fluid.DataFeedDesc('data.proto') + data_feed.set_batch_size(128) + data_feed.set_dense_slots('wd') # The slot named 'wd' will be dense + data_feed.set_use_slots('wd') # The slot named 'wd' will be used Finally, the content can be dumped out for debugging purpose: - >>> print(data_feed.desc()) + + .. code-block:: python + + print(data_feed.desc()) Args: proto_file(string): Disk file containing a data feed description. - + """ def __init__(self, proto_file): @@ -82,8 +93,28 @@ class DataFeedDesc(object): Set batch size. Will be effective during training Example: - >>> data_feed = fluid.DataFeedDesc('data.proto') - >>> data_feed.set_batch_size(128) + .. code-block:: python + + f = open("data.proto", "w") + print >> f, 'name: "MultiSlotDataFeed"' + print >> f, 'batch_size: 2' + print >> f, 'multi_slot_desc {' + print >> f, ' slots {' + print >> f, ' name: "words"' + print >> f, ' type: "uint64"' + print >> f, ' is_dense: false' + print >> f, ' is_used: true' + print >> f, ' }' + print >> f, ' slots {' + print >> f, ' name: "label"' + print >> f, ' type: "uint64"' + print >> f, ' is_dense: false' + print >> f, ' is_used: true' + print >> f, ' }' + print >> f, '}' + f.close() + data_feed = fluid.DataFeedDesc('data.proto') + data_feed.set_batch_size(128) Args: batch_size: batch size @@ -98,8 +129,28 @@ class DataFeedDesc(object): sparse slot will be fed into a LoDTensor Example: - >>> data_feed = fluid.DataFeedDesc('data.proto') - >>> data_feed.set_dense_slots(['words']) + .. code-block:: python + + f = open("data.proto", "w") + print >> f, 'name: "MultiSlotDataFeed"' + print >> f, 'batch_size: 2' + print >> f, 'multi_slot_desc {' + print >> f, ' slots {' + print >> f, ' name: "words"' + print >> f, ' type: "uint64"' + print >> f, ' is_dense: false' + print >> f, ' is_used: true' + print >> f, ' }' + print >> f, ' slots {' + print >> f, ' name: "label"' + print >> f, ' type: "uint64"' + print >> f, ' is_dense: false' + print >> f, ' is_used: true' + print >> f, ' }' + print >> f, '}' + f.close() + data_feed = fluid.DataFeedDesc('data.proto') + data_feed.set_dense_slots(['words']) Args: dense_slots_name: a list of slot names which will be set dense @@ -109,7 +160,7 @@ class DataFeedDesc(object): """ if self.proto_desc.name != "MultiSlotDataFeed": raise ValueError( - "Only MultiSlotDataFeed need set_dense_slots, pls check your datafeed.proto" + "Only MultiSlotDataFeed needs set_dense_slots, please check your datafeed.proto" ) for name in dense_slots_name: self.proto_desc.multi_slot_desc.slots[self.__name_to_index[ @@ -122,8 +173,28 @@ class DataFeedDesc(object): ones will be used for a specific model. Example: - >>> data_feed = fluid.DataFeedDesc('data.proto') - >>> data_feed.set_use_slots(['words']) + .. code-block:: python + + f = open("data.proto", "w") + print >> f, 'name: "MultiSlotDataFeed"' + print >> f, 'batch_size: 2' + print >> f, 'multi_slot_desc {' + print >> f, ' slots {' + print >> f, ' name: "words"' + print >> f, ' type: "uint64"' + print >> f, ' is_dense: false' + print >> f, ' is_used: true' + print >> f, ' }' + print >> f, ' slots {' + print >> f, ' name: "label"' + print >> f, ' type: "uint64"' + print >> f, ' is_dense: false' + print >> f, ' is_used: true' + print >> f, ' }' + print >> f, '}' + f.close() + data_feed = fluid.DataFeedDesc('data.proto') + data_feed.set_use_slots(['words']) Args: use_slots_name: a list of slot names which will be used in training @@ -133,7 +204,7 @@ class DataFeedDesc(object): """ if self.proto_desc.name != "MultiSlotDataFeed": raise ValueError( - "Only MultiSlotDataFeed need set_use_slots, pls check your datafeed.proto" + "Only MultiSlotDataFeed needs set_use_slots, please check your datafeed.proto" ) for name in use_slots_name: self.proto_desc.multi_slot_desc.slots[self.__name_to_index[ @@ -144,8 +215,28 @@ class DataFeedDesc(object): Returns a protobuf message for this DataFeedDesc Example: - >>> data_feed = fluid.DataFeedDesc('data.proto') - >>> print(data_feed.desc()) + .. code-block:: python + + f = open("data.proto", "w") + print >> f, 'name: "MultiSlotDataFeed"' + print >> f, 'batch_size: 2' + print >> f, 'multi_slot_desc {' + print >> f, ' slots {' + print >> f, ' name: "words"' + print >> f, ' type: "uint64"' + print >> f, ' is_dense: false' + print >> f, ' is_used: true' + print >> f, ' }' + print >> f, ' slots {' + print >> f, ' name: "label"' + print >> f, ' type: "uint64"' + print >> f, ' is_dense: false' + print >> f, ' is_used: true' + print >> f, ' }' + print >> f, '}' + f.close() + data_feed = fluid.DataFeedDesc('data.proto') + print(data_feed.desc()) Returns: A string message diff --git a/python/paddle/fluid/data_feeder.py b/python/paddle/fluid/data_feeder.py index 00c4e5691a23a9864ed3e8964f4cafaf9588c665..1090c781422045a2005ae1fb536a17d15005a8ad 100644 --- a/python/paddle/fluid/data_feeder.py +++ b/python/paddle/fluid/data_feeder.py @@ -21,8 +21,8 @@ import six from six.moves import zip, range, xrange import multiprocessing -from .framework import Variable, default_main_program - +from .framework import Variable, default_main_program, _current_expected_place +from .framework import _cpu_num, _cuda_ids __all__ = ['DataFeeder'] @@ -149,6 +149,7 @@ class DataFeeder(object): .. code-block:: python + import paddle.fluid as fluid place = fluid.CPUPlace() img = fluid.layers.data(name='image', shape=[1, 28, 28]) label = fluid.layers.data(name='label', shape=[1], dtype='int64') @@ -161,10 +162,16 @@ class DataFeeder(object): .. code-block:: python + import paddle + import paddle.fluid as fluid + place=fluid.CUDAPlace(0) + data = fluid.layers.data(name='data', shape=[3, 224, 224], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') + feeder = fluid.DataFeeder(place=place, feed_list=[data, label]) reader = feeder.decorate_reader( - paddle.batch(flowers.train(), batch_size=16)) + paddle.batch(paddle.dataset.flowers.train(), batch_size=16), multi_devices=False) Args: feed_list(list): The Variables or Variables'name that will @@ -180,17 +187,36 @@ class DataFeeder(object): ValueError: If some Variable is not in this Program. Examples: - .. code-block:: python + .. code-block:: python - # ... + + import numpy as np + import paddle + import paddle.fluid as fluid + place = fluid.CPUPlace() - feed_list = [ - main_program.global_block().var(var_name) for var_name in feed_vars_name - ] # feed_vars_name is a list of variables' name. - feeder = fluid.DataFeeder(feed_list, place) + + def reader(): + yield [np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32')], + + main_program = fluid.Program() + startup_program = fluid.Program() + + with fluid.program_guard(main_program, startup_program): + data_1 = fluid.layers.data(name='data_1', shape=[1, 2, 2]) + data_2 = fluid.layers.data(name='data_2', shape=[1, 1, 3]) + out = fluid.layers.fc(input=[data_1, data_2], size=2) + # ... + + feeder = fluid.DataFeeder([data_1, data_2], place) + + exe = fluid.Executor(place) + exe.run(startup_program) for data in reader(): outs = exe.run(program=main_program, - feed=feeder.feed(data)) + feed=feeder.feed(data), + fetch_list=[out]) + """ def __init__(self, feed_list, place, program=None): @@ -222,6 +248,23 @@ class DataFeeder(object): Returns: dict: the result of conversion. + + Examples: + .. code-block:: python + + import numpy.random as random + import paddle.fluid as fluid + + def reader(limit=5): + for i in range(limit): + yield random.random([784]).astype('float32'), random.random([1]).astype('int64'), random.random([256]).astype('float32') + + data_1 = fluid.layers.data(name='data_1', shape=[1, 28, 28]) + data_2 = fluid.layers.data(name='data_2', shape=[1], dtype='int64') + data_3 = fluid.layers.data(name='data_3', shape=[16, 16], dtype='float32') + feeder = fluid.DataFeeder(['data_1','data_2', 'data_3'], fluid.CPUPlace()) + + result = feeder.feed(reader()) """ converter = [] for lod_level, shape, dtype in six.moves.zip( @@ -260,6 +303,32 @@ class DataFeeder(object): Notes: The number of devices and number of mini-batches must be same. + + Examples: + .. code-block:: python + + import numpy.random as random + import paddle.fluid as fluid + + def reader(limit=10): + for i in range(limit): + yield [random.random([784]).astype('float32'), random.randint(10)], + + x = fluid.layers.data(name='x', shape=[1, 28, 28]) + y = fluid.layers.data(name='y', shape=[1], dtype='int64') + + feeder = fluid.DataFeeder(['x','y'], fluid.CPUPlace()) + place_num = 2 + places = [fluid.CPUPlace() for x in range(place_num)] + data = [] + exe = fluid.Executor(fluid.CPUPlace()) + exe.run(fluid.default_startup_program()) + program = fluid.CompiledProgram(fluid.default_main_program()).with_data_parallel(places=places) + for item in reader(): + data.append(item) + if place_num == len(data): + exe.run(program=program, feed=list(feeder.feed_parallel(data, place_num)), fetch_list=[]) + data = [] """ if isinstance(self.place, core.CUDAPlace): places = [ @@ -290,11 +359,9 @@ class DataFeeder(object): if num_places is not None: return int(num_places) elif isinstance(self.place, core.CUDAPlace): - return core.get_cuda_device_count() + return len(_cuda_ids()) else: - cpu_num = int( - os.environ.get('CPU_NUM', multiprocessing.cpu_count())) - return cpu_num + return _cpu_num() def decorate_reader(self, reader, @@ -319,6 +386,29 @@ class DataFeeder(object): Raises: ValueError: If drop_last is False and the data batch cannot fit for devices. + + Examples: + .. code-block:: python + + import numpy.random as random + import paddle + import paddle.fluid as fluid + + def reader(limit=5): + for i in range(limit): + yield (random.random([784]).astype('float32'), random.random([1]).astype('int64')), + + place=fluid.CUDAPlace(0) + data = fluid.layers.data(name='data', shape=[1, 28, 28], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') + + feeder = fluid.DataFeeder(place=place, feed_list=[data, label]) + reader = feeder.decorate_reader(reader, multi_devices=False) + + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + for data in reader(): + exe.run(feed=data) """ def __reader_creator__(): @@ -340,3 +430,63 @@ class DataFeeder(object): "not implemented") return __reader_creator__ + + +class NumpyToLoDTensorConverter(object): + def __init__(self, place): + self.place = place + self.data = [] + self._reset() + + def _reset(self): + self.data = [] + + def feed(self, data): + self.data.append(data) + + def done(self): + arr = numpy.array(self.data) + t = core.LoDTensor() + t.set(arr, self.place) + self._reset() + return t + + +class ListTensorProvider(object): + def __init__(self, generator, places): + self.generator = generator + self.converters = [] + self.places = [] + if places: + if not isinstance(places, (list, tuple)): + places = [places] + assert len( + places) == 1, "dygraph mode CAN NOT specify multiple places." + for place in places: + if isinstance(place, (core.CUDAPlace, core.CPUPlace)): + self.places.append(place) + else: + raise ValueError( + "Please specify a valid place values such as core.CPUPlace or core.CUDAPlace" + ) + if len(self.places) == 0: + self.places.append(_current_expected_place()) + + def _readData(self, iterable, places): + for place, each_sample in six.moves.zip(places, iterable): + for item in each_sample: + if len(self.converters) < len(item): + for i in item: + self.converters.append(NumpyToLoDTensorConverter(place)) + for each_converter, each_slot in six.moves.zip(self.converters, + item): + each_converter.feed(each_slot) + yield [c.done() for c in self.converters] + + def __call__(self): + item = [] + for batch in self.generator(): + item.append(batch) + if len(item) == len(self.places): + yield list(self._readData(item, self.places)) + item = [] diff --git a/python/paddle/fluid/dataset.py b/python/paddle/fluid/dataset.py index c97e0bc6e884dc2766cf57b86fe0201f04923f66..b3d58a589bd0286967abb1cee016d8d401c3a62a 100644 --- a/python/paddle/fluid/dataset.py +++ b/python/paddle/fluid/dataset.py @@ -21,27 +21,36 @@ __all__ = ['DatasetFactory', 'InMemoryDataset', 'QueueDataset'] class DatasetFactory(object): """ DatasetFactory is a factory which create dataset by its name, - you can create "QueueDataset" or "InMemoryDataset", + you can create "QueueDataset" or "InMemoryDataset", or "FileInstantDataset", the default is "QueueDataset". Example: - dataset = paddle.fluid.DatasetFactory.create_dataset("InMemoryDataset") + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") + """ def __init__(self): - """ - Init - """ + """ Init. """ pass def create_dataset(self, datafeed_class="QueueDataset"): """ - Create "QueueDataset" or "InMemoryDataset", + Create "QueueDataset" or "InMemoryDataset", or "FileInstantDataset", the default is "QueueDataset". + Args: + datafeed_class(str): datafeed class name, QueueDataset or InMemoryDataset. + Default is QueueDataset. + Examples: - import paddle.fluid as fluid - dataset = fluid.DatasetFactory().create_dataset() + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset() + """ try: dataset = globals()[datafeed_class]() @@ -52,14 +61,10 @@ class DatasetFactory(object): class DatasetBase(object): - """ - Base dataset class - """ + """ Base dataset class. """ def __init__(self): - """ - Init - """ + """ Init. """ # define class name here # to decide whether we need create in memory instance self.proto_desc = data_feed_pb2.DataFeedDesc() @@ -72,11 +77,15 @@ class DatasetBase(object): Set pipe command of current dataset A pipe command is a UNIX pipeline command that can be used only - Example: - >>> dataset.set_pipe_command("python my_script.py") + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset() + dataset.set_pipe_command("python my_script.py") Args: - pipe_command: pipe command + pipe_command(str): pipe command """ self.proto_desc.pipe_command = pipe_command @@ -85,11 +94,15 @@ class DatasetBase(object): """ Set batch size. Will be effective during training - Example: - >>> dataset.set_batch_size(128) + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset() + dataset.set_batch_size(128) Args: - batch_size: batch size + batch_size(int): batch size """ self.proto_desc.batch_size = batch_size @@ -98,11 +111,15 @@ class DatasetBase(object): """ Set thread num, it is the num of readers. - Example: - >>> dataset.set_thread(12) + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset() + dataset.set_thread(12) Args: - thread_num: thread num + thread_num(int): thread num """ self.dataset.set_thread_num(thread_num) self.thread_num = thread_num @@ -111,11 +128,15 @@ class DatasetBase(object): """ Set file list in current worker. - Example: - >>> dataset.set_filelist(['a.txt', 'b.txt']) + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset() + dataset.set_filelist(['a.txt', 'b.txt']) Args: - filelist: file list + filelist(list): file list """ self.dataset.set_filelist(filelist) @@ -123,11 +144,15 @@ class DatasetBase(object): """ Set Variables which you will use. - Example: - >>> dataset.set_use_var([data, label]) + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset() + dataset.set_use_var([data, label]) Args: - var_list: variable list + var_list(list): variable list """ multi_slot = self.proto_desc.multi_slot_desc for var in var_list: @@ -150,12 +175,16 @@ class DatasetBase(object): """ Set hdfs config: fs name ad ugi - Example: - >>> dataset.set_hdfs_config("my_fs_name", "my_fs_ugi") + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset() + dataset.set_hdfs_config("my_fs_name", "my_fs_ugi") Args: - fs_name: fs name - fs_ugi: fs ugi + fs_name(str): fs name + fs_ugi(str): fs ugi """ self.dataset.set_hdfs_config(fs_name, fs_ugi) @@ -170,8 +199,12 @@ class DatasetBase(object): """ Returns a protobuf message for this DataFeedDesc - Example: - >>> print(dataset.desc()) + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset() + print(dataset.desc()) Returns: A string message @@ -186,13 +219,11 @@ class InMemoryDataset(DatasetBase): This class should be created by DatasetFactory Example: - dataset = paddle.fluid.DatasetFactory.create_dataset("InMemoryDataset") + dataset = paddle.fluid.DatasetFactory().create_dataset("InMemoryDataset") """ def __init__(self): - """ - Init - """ + """ Init. """ super(InMemoryDataset, self).__init__() self.proto_desc.name = "MultiSlotInMemoryDataFeed" @@ -200,12 +231,14 @@ class InMemoryDataset(DatasetBase): """ Load data into memory - Example: - >>> import paddle.fluid as fluid - >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset") - >>> filelist = ["a.txt", "b.txt"] - >>> dataset.set_filelist(filelist) - >>> dataset.load_into_memory() + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") + filelist = ["a.txt", "b.txt"] + dataset.set_filelist(filelist) + dataset.load_into_memory() """ self._prepare_to_run() self.dataset.load_into_memory() @@ -214,13 +247,15 @@ class InMemoryDataset(DatasetBase): """ Local shuffle - Example: - >>> import paddle.fluid as fluid - >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset") - >>> filelist = ["a.txt", "b.txt"] - >>> dataset.set_filelist(filelist) - >>> dataset.load_into_memory() - >>> dataset.local_shuffle() + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") + filelist = ["a.txt", "b.txt"] + dataset.set_filelist(filelist) + dataset.load_into_memory() + dataset.local_shuffle() """ self.dataset.local_shuffle() @@ -232,58 +267,141 @@ class InMemoryDataset(DatasetBase): If you run in distributed mode, you should pass fleet instead of None. Examples: - >>> import paddle.fluid as fluid - >>> from paddle.fluid.incubate.fleet.pslib import fleet - >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset") - >>> filelist = ["a.txt", "b.txt"] - >>> dataset.set_filelist(filelist) - >>> dataset.load_into_memory() - >>> dataset.global_shuffle(fleet) + .. code-block:: python + + import paddle.fluid as fluid + from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") + filelist = ["a.txt", "b.txt"] + dataset.set_filelist(filelist) + dataset.load_into_memory() + dataset.global_shuffle(fleet) Args: - fleet: fleet singleton. Default None. + fleet(Fleet): fleet singleton. Default None. + """ trainer_num = 1 fleet_send_batch_size = 80000 if fleet is not None: - fleet.fleet_instance.role_maker_._barrier_worker() + fleet._role_maker._barrier_worker() trainer_num = fleet.worker_num() self.dataset.register_client2client_msg_handler() self.dataset.set_trainer_num(trainer_num) self.dataset.set_fleet_send_batch_size(fleet_send_batch_size) if fleet is not None: - fleet.fleet_instance.role_maker_._barrier_worker() + fleet._role_maker._barrier_worker() self.dataset.global_shuffle() if fleet is not None: - fleet.fleet_instance.role_maker_._barrier_worker() + fleet._role_maker._barrier_worker() def release_memory(self): """ Release InMemoryDataset memory data, when data will not be used again. - Example: - >>> import paddle.fluid as fluid - >>> import paddle.fluid.incubate.fleet.parameter_server as fleet - >>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset") - >>> filelist = ["a.txt", "b.txt"] - >>> dataset.set_filelist(filelist) - >>> dataset.load_into_memory() - >>> dataset.global_shuffle(fleet) - >>> exe = fluid.Executor(fluid.CPUPlace()) - >>> exe.run(fluid.default_startup_program()) - >>> exe.train_from_dataset(fluid.default_main_program(), dataset) - >>> dataset.release_memory() + Examples: + .. code-block:: python + + import paddle.fluid as fluid + from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") + filelist = ["a.txt", "b.txt"] + dataset.set_filelist(filelist) + dataset.load_into_memory() + dataset.global_shuffle(fleet) + exe = fluid.Executor(fluid.CPUPlace()) + exe.run(fluid.default_startup_program()) + exe.train_from_dataset(fluid.default_main_program(), dataset) + dataset.release_memory() + """ self.dataset.release_memory() + def get_memory_data_size(self, fleet=None): + """ + Get memory data size, user can call this function to know the num + of ins in all workers after load into memory. + + Note: + This function may cause bad performance, because it has barrier + + Args: + fleet(Fleet): Fleet Object. + + Returns: + The size of memory data. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") + filelist = ["a.txt", "b.txt"] + dataset.set_filelist(filelist) + dataset.load_into_memory() + print dataset.get_memory_data_size(fleet) + + """ + import numpy as np + local_data_size = self.dataset.get_memory_data_size() + local_data_size = np.array([local_data_size]) + if fleet is not None: + global_data_size = local_data_size * 0 + fleet._role_maker._node_type_comm.Allreduce(local_data_size, + global_data_size) + return global_data_size[0] + return local_data_size[0] + + def get_shuffle_data_size(self, fleet=None): + """ + Get shuffle data size, user can call this function to know the num + of ins in all workers after local/global shuffle. + + Note: + This function may cause bad performance to local shuffle, + because it has barrier. It does not affect global shuffle. + + Args: + fleet(Fleet): Fleet Object. + + Returns: + The size of shuffle data. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") + filelist = ["a.txt", "b.txt"] + dataset.set_filelist(filelist) + dataset.load_into_memory() + dataset.global_shuffle(fleet) + print dataset.get_shuffle_data_size(fleet) + + """ + import numpy as np + local_data_size = self.dataset.get_shuffle_data_size() + local_data_size = np.array([local_data_size]) + if fleet is not None: + global_data_size = local_data_size * 0 + fleet._role_maker._node_type_comm.Allreduce(local_data_size, + global_data_size) + return global_data_size[0] + return local_data_size[0] + class QueueDataset(DatasetBase): """ QueueDataset, it will process data streamly. - Example: - import paddle.fluid as fluid - dataset = fluid.DatasetFactory.create_dataset("QueueDataset") + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset("QueueDataset") + """ def __init__(self): @@ -296,10 +414,18 @@ class QueueDataset(DatasetBase): def local_shuffle(self): """ - Local shuffle + Local shuffle data. Local shuffle is not supported in QueueDataset NotImplementedError will be raised + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataset = fluid.DatasetFactory().create_dataset("QueueDataset") + dataset.local_shuffle() + """ raise NotImplementedError( "QueueDataset does not support local shuffle, " @@ -307,9 +433,53 @@ class QueueDataset(DatasetBase): def global_shuffle(self, fleet=None): """ + Global shuffle data. + Global shuffle is not supported in QueueDataset NotImplementedError will be raised + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet + dataset = fluid.DatasetFactory().create_dataset("QueueDataset") + dataset.global_shuffle(fleet) + """ raise NotImplementedError( "QueueDataset does not support global shuffle, " "please use InMemoryDataset for global_shuffle") + + +class FileInstantDataset(DatasetBase): + """ + FileInstantDataset, it will process data streamly. + Example: + import paddle.fluid as fluid + dataset = fluid.DatasetFactory.create_dataset("FileInstantDataset") + """ + + def __init__(self): + """ + Init + """ + super(FileInstantDataset, self).__init__() + self.proto_desc.name = "MultiSlotFileInstantDataFeed" + + def local_shuffle(self): + """ + Local shuffle + FileInstantDataset does not support local shuffle + """ + raise NotImplementedError( + "FileInstantDataset does not support local shuffle, " + "please use InMemoryDataset for local_shuffle") + + def global_shuffle(self, fleet=None): + """ + Global shuffle + """ + raise NotImplementedError( + "FileInstantDataset does not support global shuffle, " + "please use InMemoryDataset for global_shuffle") diff --git a/python/paddle/fluid/device_worker.py b/python/paddle/fluid/device_worker.py index 0998f779acfea23f3a494a25b43a6fa824b985f1..80989d5804da4899bd4c62b2a46cfebc4129c42b 100644 --- a/python/paddle/fluid/device_worker.py +++ b/python/paddle/fluid/device_worker.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -__all__ = ['DeviceWorker', 'Hogwild', 'DownpourSGD'] +__all__ = ['DeviceWorker', 'Hogwild', 'DownpourSGD', 'Section'] class DeviceWorker(object): @@ -155,10 +155,16 @@ class DownpourSGD(DeviceWorker): self._fleet_desc.trainer_param.sparse_table[0].slot_value) sparse_table.sparse_grad_name.extend( self._fleet_desc.trainer_param.sparse_table[0].slot_gradient) - sparse_table.emb_dim = \ - self._fleet_desc.server_param.downpour_server_param.downpour_table_param[ - 0].accessor.fea_dim - 2 - sparse_table.fea_dim = sparse_table.emb_dim + 2 + if opt_info["use_cvm"]: + sparse_table.emb_dim = \ + self._fleet_desc.server_param.downpour_server_param.downpour_table_param[ + 0].accessor.fea_dim + sparse_table.fea_dim = sparse_table.emb_dim + else: + sparse_table.emb_dim = \ + self._fleet_desc.server_param.downpour_server_param.downpour_table_param[ + 0].accessor.fea_dim - 2 + sparse_table.fea_dim = sparse_table.emb_dim + 2 # TODO(guru4elephant): hard code here, need to improve sparse_table.label_var_name = "click" @@ -175,6 +181,58 @@ class DownpourSGD(DeviceWorker): downpour.push_sparse = False +class Section(DeviceWorker): + """ + SectionWorker + """ + + def __init__(self): + """ + Init. + """ + super(Section, self).__init__() + + def _gen_worker_desc(self, trainer_desc): + """ + Generator worker desc, which device worker is SectionWorker. + Args: + trainer_desc(TrainerDesc): a TrainerDesc object + """ + from google.protobuf import text_format + from . import core + trainer_desc.device_worker_name = "SectionWorker" + pipeline_opt = self._program._pipeline_opt + section_param = trainer_desc.section_param + section_param.queue_size = pipeline_opt["queue_size"] + section_param.sync_steps = pipeline_opt["sync_steps"] + section_param.start_cpu_core_id = pipeline_opt["start_cpu_core_id"] + for e in pipeline_opt["param_need_sync"]: + section_param.param_need_sync.append(e) + for i, program in enumerate(pipeline_opt["section_program_list"]): + cfg = section_param.section_config.add() + cfg.program_desc.ParseFromString(program["program"]._get_desc() + .serialize_to_string()) + # TODO: why does not work + #cfg.program_desc.CopyFrom(program.program._get_desc()) + place = pipeline_opt["place_list"][i] + if isinstance(place, core.CPUPlace): + cfg.place = cfg.CPUPlace + elif isinstance(place, core.CUDAPlace): + cfg.place = cfg.CUDAPlace + elif isinstance(place, core.CUDAPinnedPlace): + cfg.place = cfg.CUDAPinnedPlace + else: + raise NotImplementedError( + "SectionWorker only supports CPUPlace, CUDAPlace and CUDAPinnedPlace now." + ) + + cfg.concurrency = pipeline_opt["concurrency_list"][i] + for var in program["input_set"]: + cfg.section_in_var_names.append(var) + for var in program["output_set"]: + cfg.section_out_var_names.append(var) + + class DeviceWorkerFactory(object): def _create_device_worker(self, worker_type): classname = worker_type.capitalize() diff --git a/python/paddle/fluid/dygraph/__init__.py b/python/paddle/fluid/dygraph/__init__.py index 9bb72ede304dbde732153bac980f24a74bcd126d..7ab1dfdf7677497d9a25c7962e48e1be13da56c8 100644 --- a/python/paddle/fluid/dygraph/__init__.py +++ b/python/paddle/fluid/dygraph/__init__.py @@ -38,6 +38,9 @@ from .checkpoint import * from . import learning_rate_scheduler from .learning_rate_scheduler import * +from . import backward_strategy +from .backward_strategy import * + __all__ = [] __all__ += layers.__all__ __all__ += base.__all__ @@ -47,3 +50,4 @@ __all__ += profiler.__all__ __all__ += parallel.__all__ __all__ += checkpoint.__all__ __all__ += learning_rate_scheduler.__all__ +__all__ += backward_strategy.__all__ diff --git a/python/paddle/fluid/dygraph/base.py b/python/paddle/fluid/dygraph/base.py index bf484b35c7bf9a2b17126789ff247bd73095fe7b..133eb6a19c2e20287ef6588cc2c4f780ec7dbdd4 100644 --- a/python/paddle/fluid/dygraph/base.py +++ b/python/paddle/fluid/dygraph/base.py @@ -11,22 +11,116 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from ..wrapped_decorator import signature_safe_contextmanager +from ..wrapped_decorator import signature_safe_contextmanager, wrap_decorator +import contextlib import numpy as np +import os from paddle.fluid import core from paddle.fluid import framework from .tracer import Tracer +import logging -__all__ = ['enabled', 'guard', 'to_variable'] +__all__ = [ + 'enabled', + 'no_grad', + 'not_support', + 'guard', + 'to_variable', +] def enabled(): return framework.in_dygraph_mode() +@contextlib.contextmanager +def _switch_tracer_mode_guard_(is_train=True): + tracer = framework._dygraph_tracer() + if tracer: + mode = tracer._train_mode + tracer._train_mode = is_train + yield + tracer._train_mode = mode + else: + yield + + +def _dygraph_not_support_(func): + def __impl__(*args, **kwargs): + assert not framework.in_dygraph_mode( + ), "We don't support %s in Dygraph mode" % func.__name__ + return func(*args, **kwargs) + + return __impl__ + + +def _no_grad_(func): + """ + This Decorator will avoid the func being decorated creating backward network in dygraph mode + + Args: + func: the func don't need grad + + Examples: + + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + + @fluid.dygraph.no_grad + def test_layer(): + with fluid.dygraph.guard(): + inp = np.ones([3, 32, 32], dtype='float32') + t = fluid.dygraph.base.to_variable(inp) + fc1 = fluid.FC('fc1', size=4, bias_attr=False, num_flatten_dims=1) + fc2 = fluid.FC('fc2', size=4) + ret = fc1(t) + dy_ret = fc2(ret) + + test_layer() + + """ + + def __impl__(*args, **kwargs): + with _switch_tracer_mode_guard_(is_train=False): + return func(*args, **kwargs) + + return __impl__ + + +no_grad = wrap_decorator(_no_grad_) +not_support = wrap_decorator(_dygraph_not_support_) + + @signature_safe_contextmanager def guard(place=None): + """ + This context will create a dygraph context for dygraph to run + + Args: + place(fluid.CPUPlace|fluid.CUDAPlace|None): Place to run + + return: + None + + Examples: + + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + + with fluid.dygraph.guard(): + inp = np.ones([3, 32, 32], dtype='float32') + t = fluid.dygraph.base.to_variable(inp) + fc1 = fluid.FC('fc1', size=4, bias_attr=False, num_flatten_dims=1) + fc2 = fluid.FC('fc2', size=4) + ret = fc1(t) + dy_ret = fc2(ret) + + """ train = framework.Program() startup = framework.Program() tracer = Tracer(train.current_block().desc) @@ -44,7 +138,45 @@ def guard(place=None): yield +def _print_debug_msg(): + if not core._is_dygraph_debug_enabled(): + logging.warn( + 'Debug mode is not enabled. Please set FLAGS_dygraph_debug=1 to enable debug' + ) + return + + unique_name_size = len(framework.unique_name.generator.ids) + tracer_var_size = len(framework._dygraph_tracer()._vars) + alive_cpp_var_size = len(core.VarBase._alive_vars()) + logging.warn( + 'unique_name num: {}, tracer vars num: {}, alive cpp vars num: {}' + .format(unique_name_size, tracer_var_size, alive_cpp_var_size)) + + def to_variable(value, block=None, name=None): + """ + This function will create a variable from ndarray + + Args: + value(ndarray): the numpy value need to be convert + block(fluid.Block|None): which block this variable will be in + name(str|None): Name of Varaible + + return: + Variable: The variable created from given numpy + + Examples: + + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + + with fluid.dygraph.guard(): + x = np.ones([2, 2], np.float32) + y = fluid.dygraph.to_variable(x) + + """ if isinstance(value, np.ndarray): assert enabled(), "to_variable could only be called in dygraph mode" @@ -63,3 +195,6 @@ def to_variable(value, block=None, name=None): return py_var elif isinstance(value, framework.Variable): return value + else: + raise TypeError( + "to_variable only accepts 'ndarray' and 'Variable' as value's input") diff --git a/python/paddle/fluid/dygraph/checkpoint.py b/python/paddle/fluid/dygraph/checkpoint.py index f96b53e8c0b1e6ee93a14ecc811cd32a01bc7702..52849405558358041b45b870dd5eb54898766f50 100644 --- a/python/paddle/fluid/dygraph/checkpoint.py +++ b/python/paddle/fluid/dygraph/checkpoint.py @@ -1,4 +1,4 @@ -# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -16,16 +16,18 @@ from __future__ import print_function import os import collections -from .. import core from ..framework import Variable, default_main_program +import pickle +from . import learning_rate_scheduler +import warnings __all__ = ['save_persistables', 'load_persistables'] -def save_persistables(vardict, dirname, filename=None): +def save_persistables(model_dict, dirname='save_dir', optimizers=None): """ This function filters out all variables in layer.parameters from the - give `layer` and then trys to load these variables from the folder + give `layer`, and optimizer's learning rate decay and then trys to load these variables from the folder `dirname` or the file `filename`. Use the `dirname` to specify the folder where persistable variables were @@ -34,13 +36,11 @@ def save_persistables(vardict, dirname, filename=None): the file name. Args: - vardict(dict of Parameters): The parameters will + model_dict(dict of Parameters): The parameters will be saved. If it is None, nothing will be deal. dirname(str): The directory path. - filename(str|None): The file which saved all variables. If variables were - saved in differnet files, set it to None. - Default: None + optimizers(fluid.Optimizer|list(fluid.Optimizer)|None): The optimizers to be saved Returns: @@ -52,7 +52,7 @@ def save_persistables(vardict, dirname, filename=None): num_layers=num_layers, num_steps=num_steps, init_scale=init_scale) - + sgd = fluid.optimizer.SGD(learning_rate=0.01) x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) @@ -67,15 +67,17 @@ def save_persistables(vardict, dirname, filename=None): init_cell = to_variable(init_cell_data) dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden, init_cell) + dy_loss.backward() + sgd.minimize(dy_loss) + ptb_model.clear_gradient() param_path = "./my_paddle_model" - fluid.dygraph.save_persistables(ptb_model.state_dict(), dirname=param_path, - layer=ptb_model) + fluid.dygraph.save_persistables(ptb_model.state_dict(), dirname=param_path, sgd) """ - if isinstance(vardict, collections.OrderedDict): - _save_var_to_file(vardict, dirname, filename) + if isinstance(model_dict, collections.OrderedDict): + _save_var_to_file(model_dict, optimizers, dirname, None) -def load_persistables(dirname): +def load_persistables(dirname='save_dir'): """ This function trys to load persistable variables from the folder `dirname` or the file `filename`. @@ -86,24 +88,26 @@ def load_persistables(dirname): the file name. Args: - dirname(str): The directory path. + dirname(str): The directory path. default is save_dir Returns: dict: The parameter-dict resumed from file + optimizer dict: The optimizer Examples: .. code-block:: python my_layer = layer(fluid.Layer) param_path = "./my_paddle_model" - - param_dict = fluid.dygraph.load_persistables(my_layer.parameters(), param_path) + sgd = SGDOptimizer(learning_rate=1e-3) + param_dict, optimizer_dict = fluid.dygraph.load_persistables(my_layer.parameters(), param_path) param_1 = param_dict['PtbModel_0.w_1'] + sgd.load(optimizer_dict) """ return _load_var_from_file(dirname) -def _save_var_to_file(stat_dict, file_dir, file_name): +def _save_var_to_file(stat_dict, optimizers, file_dir, file_name): save_block = default_main_program().global_block() save_var_map = {} for var_key, each_var in stat_dict.items(): @@ -118,6 +122,38 @@ def _save_var_to_file(stat_dict, file_dir, file_name): os.path.normpath(each_var.name)) }) + if optimizers is not None: + if isinstance(optimizers, (list, tuple)): + optimizers = optimizers + else: + optimizers = [optimizers] + if os.path.exists( + os.path.join(file_dir, os.path.normpath("optimizers"))): + pass + else: + os.mkdir(os.path.join(file_dir, os.path.normpath("optimizers"))) + for optimizer in optimizers: + if isinstance(optimizer._learning_rate, + learning_rate_scheduler.LearningRateDecay): + try: + f = open( + os.path.join(file_dir, "optimizers", + os.path.normpath(str(optimizer._name))), + "wb") + pickle.dump(optimizer._learning_rate, f, 2) + f.close() + except (): + raise IOError("Can't load %s", + os.path.join( + file_dir, "optimizers", + os.path.normpath(str(optimizer._name)))) + else: + warnings.warn( + "Optimizer not saved, Only optimizer with 'LearningRateDecay' under DyGraph mode need to be saved" + ) + else: + pass + if file_name is not None: save_var_list = [] for name in sorted(save_var_map.keys()): @@ -138,6 +174,8 @@ def _load_var_from_file(file_dir): var_name_list = [] if os.path.exists(base_path): for dirpath, dirnames, filenames in os.walk(base_path): + if "optimizers" in dirpath: + continue pt = dirpath.replace(base_path, "", 1) if pt.startswith("/") or pt.startswith("\\"): pt = pt[1:] @@ -152,6 +190,7 @@ def _load_var_from_file(file_dir): load_block = default_main_program().global_block() load_var_map = {} + load_optimizer_map = {} file_var_list = walk_filename(file_dir) for var_name in file_var_list: new_var = Variable(block=load_block, name=var_name) @@ -165,8 +204,25 @@ def _load_var_from_file(file_dir): }) load_var_map[new_var.name] = new_var - - return load_var_map + opt_path = os.path.join(file_dir, "optimizers") + for _, _, optimizers in os.walk(opt_path): + for optimizer in optimizers: + try: + f = open(os.path.join(opt_path, optimizer), "rb") + load_optimizer_map[optimizer] = pickle.load(f) + f.close() + except IOError: + raise IOError("Can't load %s", + os.path.join( + file_dir, "optimizers", + os.path.normpath(str(optimizer._name)))) + if len(load_optimizer_map) == 0: + print( + "No optimizer loaded. If you didn't save optimizer, please ignore this. The program can still work with new optimizer. " + ) + pass + + return load_var_map, load_optimizer_map def _clone_var_in_block_(block, var): diff --git a/python/paddle/fluid/dygraph/layers.py b/python/paddle/fluid/dygraph/layers.py index 7ddf94146c776e4e62b106f87004df52e891bf62..eca8d060b0f2f79d30ec8abda57aeeb2677d8c16 100644 --- a/python/paddle/fluid/dygraph/layers.py +++ b/python/paddle/fluid/dygraph/layers.py @@ -18,13 +18,14 @@ import sys import numpy as np import collections import six +from . import parallel_helper from .. import unique_name from paddle.fluid import core from .layer_object_helper import LayerObjectHelper from paddle.fluid import framework from ..param_attr import ParamAttr -__all__ = ['Layer', 'PyLayer'] +__all__ = ['Layer'] class Layer(core.Layer): @@ -146,14 +147,17 @@ class Layer(core.Layer): def clear_gradients(self): for p in self.parameters(): - p.clear_gradient() + if p.trainable: + p.clear_gradient() - def build_once(self, *args): + def _build_once(self, *args): pass def __call__(self, *inputs): if not self._built: - self.build_once(*inputs) + self._build_once(*inputs) + if parallel_helper._is_data_parallel_mode(): + parallel_helper._broadcast_parameters(self._parameters.values()) outputs = self.forward(*inputs) self._built = True @@ -193,10 +197,14 @@ class Layer(core.Layer): the parameter passed in. """ assert isinstance(parameter, framework.Parameter) - self._parameters[name] = parameter + if parameter.name in self._loaddict_holder: - self._parameters[name] = self._loaddict_holder[parameter.name] - parameter = self._loaddict_holder[parameter.name] + var = parameter._ivar.value() + tensor = var.get_tensor() + tensor.set(self._loaddict_holder[parameter.name].numpy(), + framework._current_expected_place()) + + self._parameters[name] = parameter return parameter def __getattr__(self, name): @@ -212,9 +220,11 @@ class Layer(core.Layer): raise ValueError( "super(YourLayer, self).__init__() should be called first") if value.name in self._loaddict_holder: - params[name] = self._loaddict_holder[value.name] - else: - params[name] = value + var = value._ivar.value() + tensor = var.get_tensor() + tensor.set(self._loaddict_holder[value.name].numpy(), + framework._current_expected_place()) + params[name] = value elif isinstance(value, core.Layer): layers = self.__dict__.get('_sub_layers', None) if layers is None: @@ -232,20 +242,19 @@ class Layer(core.Layer): else: object.__delattr__(self, name) - def state_dict(self, destination=None, prefix='', include_sublayers=True): + def state_dict(self, destination=None, include_sublayers=True): if destination is None: destination = collections.OrderedDict() for name, data in self._parameters.items(): if data is not None: - destination[prefix + name] = data + destination[data.name] = data if include_sublayers: for layer_name, layer_item in self._sub_layers.items(): if layer_item is not None: destination_temp = destination.copy() destination_temp.update( - layer_item.state_dict(destination_temp, prefix + - layer_name + ".", + layer_item.state_dict(destination_temp, include_sublayers)) destination = destination_temp return destination @@ -263,76 +272,3 @@ class Layer(core.Layer): for layer_name, layer_item in self._sub_layers.items(): if layer_item is not None: layer_item.load_dict(stat_dict) - - -class PyLayer(core.PyLayer): - """Layers composed of user-defined python codes.""" - - def __init__(self): - super(PyLayer, self).__init__() - - def train(self): - framework._dygraph_tracer().train_mode() - - def eval(self): - framework._dygraph_tracer().eval_mode() - - @classmethod - def _do_forward(cls, inputs): - return cls._to_tuple(cls.forward(inputs)) - - @classmethod - def _do_backward(cls, inputs): - return cls._to_tuple(cls.backward(inputs)) - - @staticmethod - def _to_tuple(inputs): - if not isinstance(inputs, list) and not isinstance(inputs, tuple): - inputs = [inputs] - ret = [] - for inp in inputs: - if isinstance(inp, core.LoDTensor): - ret.append(inp) - else: - tensor = core.LoDTensor() - tensor.set(inp, core.CPUPlace()) - ret.append(tensor) - return tuple(ret) - - @staticmethod - def forward(*inputs): - raise NotImplementedError - - @staticmethod - def backward(*douts): - raise NotImplementedError - - @classmethod - def __call__(cls, *inputs): - tracer = framework._dygraph_tracer() - block = framework.default_main_program().current_block() - ivar_inputs = [x._ivar for x in inputs] - - if not hasattr(cls, 'forward_id'): - cls.forward_id = core.PyLayer.num_funcs() + 1 - PyLayer.register_func(cls.forward_id, cls._do_forward) - cls.backward_id = core.PyLayer.num_funcs() + 1 - PyLayer.register_func(cls.backward_id, cls._do_backward) - - iop = core.OpBase(cls.__class__.__name__ + str(cls.forward_id)) - iop.forward_id = cls.forward_id - iop.backward_id = cls.backward_id - block.ops.append(iop) - ivars = tracer.py_trace(iop, ivar_inputs, False) - ret = [] - for ivar in ivars: - tensor = ivar.value().get_tensor() - py_var = framework.Variable( - block, - type=core.VarDesc.VarType.LOD_TENSOR, - name=None, - shape=tensor.shape(), - dtype=tensor._dtype(), - ivar=ivar) - ret.append(py_var) - return ret diff --git a/python/paddle/fluid/dygraph/learning_rate_scheduler.py b/python/paddle/fluid/dygraph/learning_rate_scheduler.py index 3209fa76d95c35c6c5a1bb36801b9f9354b1a927..500ab63b0e0e5d4166abe15ac326eb921a0fa00f 100644 --- a/python/paddle/fluid/dygraph/learning_rate_scheduler.py +++ b/python/paddle/fluid/dygraph/learning_rate_scheduler.py @@ -27,6 +27,10 @@ __all__ = [ class LearningRateDecay(object): """ Base class of learning rate decay + + Define the common interface of an LearningRateDecay. + User should not use this class directly, + but need to use one of it's implementation. """ def __init__(self, begin=0, step=1, dtype='float32'): @@ -42,13 +46,21 @@ class LearningRateDecay(object): return lr def create_lr_var(self, lr): + """ + convert lr from float to variable + + Args: + lr: learning rate + Returns: + learning rate variable + """ from .. import layers lr = layers.create_global_var( name=unique_name.generate("learning_rate"), shape=[1], value=float(lr), dtype=self.dtype, - persistable=True) + persistable=False) return lr def step(self): @@ -56,6 +68,40 @@ class LearningRateDecay(object): class PiecewiseDecay(LearningRateDecay): + """ + piecewise decay scheduler + + The algorithm can be described as the code below. + + .. code-block:: text + + boundaries = [10000, 20000] + values = [1.0, 0.5, 0.1] + if step < 10000: + learning_rate = 1.0 + elif 10000 <= step < 20000: + learning_rate = 0.5 + else: + learning_rate = 0.1 + Args: + boundaries: A list of steps numbers. + values: A list of learning rate values that will be picked during + different step boundaries. + begin: The begin step to initilize the self.step_num + step: The step_size using when calculate the new step_num (Defalult is 1) + dtype: The dtype used to create the learning rate variable + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + boundaries = [10000, 20000] + values = [1.0, 0.5, 0.1] + with fluid.dygraph.guard(): + optimizer = fluid.optimizer.SGD( + learning_rate=fluid.dygraph.PiecewiseDecay(boundaries, values, 0) ) + """ + def __init__(self, boundaries, values, begin, step=1, dtype='float32'): super(PiecewiseDecay, self).__init__(begin, step, dtype) self.boundaries = boundaries @@ -63,16 +109,51 @@ class PiecewiseDecay(LearningRateDecay): self.vars = [] for value in values: - self.vars.append(self.create_lr_var(value)) + self.vars.append(value) def step(self): for i in range(len(self.boundaries)): if self.step_num < self.boundaries[i]: return self.vars[i] - return self.vars[len(self.values) - 1] + return self.create_lr_var(self.vars[len(self.values) - 1]) class NaturalExpDecay(LearningRateDecay): + """ + Applies natural exponential decay to the initial learning rate. + + .. code-block:: python + + if not staircase: + decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps)) + else: + decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps)) + + Args: + learning_rate: A scalar float32 value or a Variable. This + will be the initial learning rate during training + decay_steps: A Python `int32` number. + decay_rate: A Python `float` number. + staircase: Boolean. If set true, decay the learning rate every decay_steps. + begin: A Python 'int32' number, the begin step (Default is 0) + step: A Python 'int32' number, the step size (Default is 1) + dtype: A Python 'str', the dtype used to create learning rate variable (Default is 'float32') + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + base_lr = 0.1 + with fluid.dygraph.guard(): + sgd_optimizer = fluid.optimizer.SGD( + learning_rate=fluid.dygraph.NaturalExpDecay( + learning_rate=base_lr, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) + + """ + def __init__(self, learning_rate, decay_steps, @@ -99,6 +180,45 @@ class NaturalExpDecay(LearningRateDecay): class ExponentialDecay(LearningRateDecay): + """ + Applies exponential decay to the learning rate. + + When training a model, it is often recommended to lower the learning rate as the + training progresses. By using this function, the learning rate will be decayed by + 'decay_rate' every 'decay_steps' steps. + + .. code-block:: python + + if staircase == True: + decayed_learning_rate = learning_rate * decay_rate ^ floor(global_step / decay_steps) + else: + decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps) + + Args: + learning_rate(Variable|float): The initial learning rate. + decay_steps(int): See the decay computation above. + decay_rate(float): The decay rate. See the decay computation above. + staircase(Boolean): If True, decay the learning rate at discrete intervals. + Default: False + begin(int): The begin step (default is 0) + step(int): The step size (default is 1) + dtype(str): The dtype used to create learning rate (default is 'float32') + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + base_lr = 0.1 + with fluid.dygraph.guard(): + sgd_optimizer = fluid.optimizer.SGD( + learning_rate=fluid.dygraph.ExponentialDecay( + learning_rate=base_lr, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) + + """ + def __init__(self, learning_rate, decay_steps, @@ -125,6 +245,43 @@ class ExponentialDecay(LearningRateDecay): class InverseTimeDecay(LearningRateDecay): + """ + Applies inverse time decay to the initial learning rate. + + When training a model, it is often recommended to lower the learning rate as the + training progresses. By using this function, an inverse decay function will be + applied to the initial learning rate. + + >>> if staircase == True: + >>> decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step)) + >>> else: + >>> decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step) + + Args: + learning_rate(Variable|float): The initial learning rate. + decay_steps(int): See the decay computation above. + decay_rate(float): The decay rate. See the decay computation above. + staircase(Boolean): If True, decay the learning rate at discrete intervals. + Default: False + begin(int): The begin step (default is 0) + step(int): The step size (default is 1) + dtype(str): The dtype used to create learning rate (default is 'float32') + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + base_lr = 0.1 + with fluid.dygraph.guard(): + sgd_optimizer = fluid.optimizer.SGD( + learning_rate=fluid.dygraph.InverseTimeDecay( + learning_rate=base_lr, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) + + """ + def __init__(self, learning_rate, decay_steps, @@ -151,6 +308,43 @@ class InverseTimeDecay(LearningRateDecay): class PolynomialDecay(LearningRateDecay): + """ + Applies polynomial decay to the initial learning rate. + + .. code-block:: text + + if cycle: + decay_steps = decay_steps * ceil(global_step / decay_steps) + else: + global_step = min(global_step, decay_steps) + decayed_learning_rate = (learning_rate - end_learning_rate) * + (1 - global_step / decay_steps) ^ power + end_learning_rate + + Args: + learning_rate(Variable|float32): A scalar float32 value or a Variable. This + will be the initial learning rate during training. + decay_steps(int32): A Python `int32` number. + end_learning_rate(float): A Python `float` number. + power(float): A Python `float` number. + cycle(bool): If set true, decay the learning rate every decay_steps. + begin(int): The begin step (default is 0) + step(int): The step size (default is 1) + dtype(str): The dtype used to create learning rate (default is 'float32') + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + start_lr = 0.01 + total_step = 5000 + end_lr = 0 + with fluid.dygraph.guard(): + optimizer = fluid.optimizer.SGD( + learning_rate = fluid.dygraph.PolynomialDecay( + start_lr, total_step, end_lr, power=1.0) ) + + """ + def __init__(self, learning_rate, decay_steps, @@ -189,6 +383,35 @@ class PolynomialDecay(LearningRateDecay): class CosineDecay(LearningRateDecay): + """ + Applies cosine decay to the learning rate. + + when training a model, it is often recommended to lower the learning rate as the + training progresses. By using this function, the learning rate will be decayed by + following cosine decay strategy. + + .. math:: + + decayed\_lr = learning\_rate * 0.5 * (math.cos * (epoch * \\frac{math.pi}{epochs} ) + 1) + + Args: + learning_rate(Variable|float): The initial learning rate. + step_each_epoch(int): the number of steps in an epoch. + epochs(int): the number of epochs. + begin(int): The begin step (default is 0). + step(int): The step size (default is 1). + dtype(str): The dtype used to create learning rate (default is 'float32'). + + Examples: + .. code-block:: python + + base_lr = 0.1 + with fluid.dygraph.guard(): + optimizer = fluid.optimizer.SGD( + learning_rate = fluid.dygraph.CosineDecay( + base_lr, 10000, 120) ) + """ + def __init__(self, learning_rate, step_each_epoch, @@ -211,6 +434,45 @@ class CosineDecay(LearningRateDecay): class NoamDecay(LearningRateDecay): + """ + Noam decay method. The numpy implementation of noam decay as follows. + + .. code-block:: python + + import numpy as np + # set hyper parameters + d_model = 2 + current_steps = 20 + warmup_steps = 200 + # compute + lr_value = np.power(d_model, -0.5) * np.min([ + np.power(current_steps, -0.5), + np.power(warmup_steps, -1.5) * current_steps]) + + Please reference `attention is all you need + `_. + + Args: + d_model(Variable): The dimensionality of input and output of model. + + warmup_steps(Variable): A super parameter. + begin(int): The begin step (default is 0) + step(int): The step size (default is 1) + dtype(str): The dtype used to create learning rate (default is 'float32') + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + warmup_steps = 100 + learning_rate = 0.01 + with fluid.dygraph.guard(): + optimizer = fluid.optimizer.SGD( + learning_rate = fluid.dygraph.NoamDecay( + 1/(warmup_steps *(learning_rate ** 2)), + warmup_steps) ) + """ + def __init__(self, d_model, warmup_steps, begin=1, step=1, dtype='float32'): super(NoamDecay, self).__init__(begin, step, dtype) self.d_model = d_model diff --git a/python/paddle/fluid/dygraph/nn.py b/python/paddle/fluid/dygraph/nn.py index d6360fedd4756b6765e141b9e31b08d8ddcf0f5e..3fa74d78f5f8f2fd601105fc5fc04eaf2a1fc4f1 100644 --- a/python/paddle/fluid/dygraph/nn.py +++ b/python/paddle/fluid/dygraph/nn.py @@ -27,8 +27,7 @@ import numpy as np __all__ = [ 'Conv2D', 'Conv3D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding', 'GRUUnit', 'LayerNorm', 'NCE', 'PRelu', 'BilinearTensorProduct', 'Conv2DTranspose', - 'Conv3DTranspose', 'SequenceConv', 'RowConv', 'GroupNorm', 'SpectralNorm', - 'TreeConv' + 'Conv3DTranspose', 'GroupNorm', 'SpectralNorm', 'TreeConv' ] @@ -84,7 +83,7 @@ class Conv2D(layers.Layer): W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1 Args: - input (Variable): The input image with [N, C, H, W] format. + name_scope(str) : The name for this class. num_filters(int): The number of filter. It is as same as the output image channel. filter_size (int|tuple|None): The filter size. If filter_size is a tuple, @@ -118,12 +117,6 @@ class Conv2D(layers.Layer): library is installed. Default: True act (str): Activation type, if it is set to None, activation is not appended. Default: None - name (str|None): A name for this layer(optional). If set None, the layer - will be named automatically. Default: None - - Returns: - Variable: The tensor variable storing the convolution and \ - non-linearity activation result. Raises: ValueError: If the shapes of input, filter_size, stride, padding and @@ -131,25 +124,37 @@ class Conv2D(layers.Layer): Examples: .. code-block:: python + + with fluid.dygraph.guard(): + conv2d = Conv2D( "conv2d", 2, 3) + data = to_variable( data ) + conv = conv2d( data ) + from paddle.fluid.dygraph.base import to_variable + import paddle.fluid as fluid + from paddle.fluid.dygraph import Conv2D + import numpy as np + + data = np.random.uniform( -1, 1, [10, 3, 32, 32] ).astype('float32') + with fluid.dygraph.guard(): + conv2d = Conv2D( "conv2d", 2, 3) + data = to_variable( data ) + conv = conv2d( data ) - data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32') - conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu") """ def __init__(self, name_scope, - num_channels, num_filters, filter_size, stride=1, padding=0, dilation=1, groups=None, - use_cudnn=True, - act=None, param_attr=None, bias_attr=None, - dtype=core.VarDesc.VarType.FP32): + use_cudnn=True, + act=None, + dtype='float32'): assert param_attr is not False, "param_attr should not be False here." super(Conv2D, self).__init__(name_scope, dtype) self._groups = groups @@ -160,7 +165,11 @@ class Conv2D(layers.Layer): if not isinstance(use_cudnn, bool): raise ValueError("use_cudnn should be True or False") self._use_cudnn = use_cudnn - self._num_channels = num_channels + self._filter_size = filter_size + self._num_filters = num_filters + self._param_attr = param_attr + self._bias_attr = bias_attr + self._dtype = dtype # if (self._num_channels == self._groups and # num_filters % self._num_channels == 0 and not self._use_cudnn): # self._l_type = 'depthwise_conv2d' @@ -169,22 +178,26 @@ class Conv2D(layers.Layer): # kernel fixed https://github.com/PaddlePaddle/Paddle/issues/17275 self._l_type = 'conv2d' - if groups is None: - num_filter_channels = num_channels + def _build_once(self, input): + self._num_channels = input.shape[1] + if self._groups is None: + num_filter_channels = self._num_channels else: - if num_channels % groups != 0: + if self._num_channels % self._groups != 0: raise ValueError("num_channels must be divisible by groups.") - num_filter_channels = num_channels // groups - filter_size = utils.convert_to_list(filter_size, 2, 'filter_size') - filter_shape = [num_filters, int(num_filter_channels)] + filter_size + num_filter_channels = self._num_channels // self._groups + filter_size = utils.convert_to_list(self._filter_size, 2, 'filter_size') + filter_shape = [self._num_filters, int(num_filter_channels) + ] + filter_size def _get_default_param_initializer(): - filter_elem_num = filter_size[0] * filter_size[1] * num_channels + filter_elem_num = filter_size[0] * filter_size[ + 1] * self._num_channels std = (2.0 / filter_elem_num)**0.5 return Normal(0.0, std, 0) self._filter_param = self.create_parameter( - attr=param_attr, + attr=self._param_attr, shape=filter_shape, dtype=self._dtype, default_initializer=_get_default_param_initializer()) @@ -204,8 +217,8 @@ class Conv2D(layers.Layer): type=core.VarDesc.VarType.RAW) self._bias_param = self.create_parameter( - attr=bias_attr, - shape=[num_filters], + attr=self._bias_attr, + shape=[self._num_filters], dtype=self._dtype, is_bias=True) @@ -229,15 +242,17 @@ class Conv2D(layers.Layer): 'use_mkldnn': False, }) - pre_act = self._helper.create_variable_for_type_inference( - dtype=self._dtype) - - self._helper.append_op( - type='elementwise_add', - inputs={'X': [pre_bias], - 'Y': [self._bias_param]}, - outputs={'Out': [pre_act]}, - attrs={'axis': 1}) + if self._bias_param is not None: + pre_act = self._helper.create_variable_for_type_inference( + dtype=self._dtype) + self._helper.append_op( + type='elementwise_add', + inputs={'X': [pre_bias], + 'Y': [self._bias_param]}, + outputs={'Out': [pre_act]}, + attrs={'axis': 1}) + else: + pre_act = pre_bias # Currently, we don't support inplace in dygraph mode return self._helper.append_activation(pre_act, act=self._act) @@ -339,8 +354,16 @@ class Conv3D(layers.Layer): Examples: .. code-block:: python - data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32') - conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu") + import paddle.fluid as fluid + import numpy + + with fluid.dygraph.guard(): + data = numpy.random.random((5, 3, 12, 32, 32)).astype('float32') + + conv3d = fluid.dygraph.nn.Conv3D( + 'Conv3D', num_filters=2, filter_size=3, act="relu") + ret = conv3d(fluid.dygraph.base.to_variable(data)) + """ def __init__(self, @@ -370,7 +393,7 @@ class Conv3D(layers.Layer): self._param_attr = param_attr self._bias_attr = bias_attr - def build_once(self, input): + def _build_once(self, input): num_channels = input.shape[1] self._dtype = self._helper.input_dtype(input) @@ -539,12 +562,19 @@ class Conv3DTranspose(layers.Layer): Examples: .. code-block:: python - conv3d_transpose = nn.Conv3DTranspose( - 'Conv3DTranspose', - num_filters=12, - filter_size=12, - use_cudnn=False) - transpose_res = conv3d_transpose(base.to_variable(input_array)) + import paddle.fluid as fluid + import numpy + + with fluid.dygraph.guard(): + data = numpy.random.random((5, 3, 12, 32, 32)).astype('float32') + + conv3dTranspose = fluid.dygraph.nn.Conv3DTranspose( + 'Conv3DTranspose', + num_filters=12, + filter_size=12, + use_cudnn=False) + ret = conv3dTranspose(fluid.dygraph.base.to_variable(data)) + """ def __init__(self, @@ -577,7 +607,7 @@ class Conv3DTranspose(layers.Layer): self._bias_attr = bias_attr self._act = act - def build_once(self, input): + def _build_once(self, input): self._dtype = self._helper.input_dtype(input) self._input_channel = input.shape[1] @@ -651,14 +681,12 @@ class Conv3DTranspose(layers.Layer): class Pool2D(layers.Layer): + # TODO, should delete this class """ ${comment} Args: - input (Variable): The input tensor of pooling operator. The format of - input tensor is NCHW, where N is batch size, C is - the number of channels, H is the height of the - feature, and W is the width of the feature. + name_scope(str) : The name of this class. pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain two integers, (pool_size_Height, pool_size_Width). Otherwise, the pool kernel size will be a square of an int. @@ -812,8 +840,7 @@ class FC(layers.Layer): out.shape = (1, 2) Args: - input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of - the input tensor(s) is at least 2. + name(str): The name of this class. size(int): The number of output units in this layer. num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than two dimensions. If this happens, the multidimensional tensor will first be flattened @@ -831,37 +858,35 @@ class FC(layers.Layer): If it is set to None, the bias is initialized zero. Default: None. act (str, default None): Activation to be applied to the output of this layer. is_test(bool): A flag indicating whether execution is in test phase. - name (str, default None): The name of this layer. - - Returns: - Variable: The transformation result. + dtype(str): Dtype used for weight Raises: ValueError: If rank of the input tensor is less than 2. Examples: .. code-block:: python + + from paddle.fluid.dygraph.base import to_variable + import paddle.fluid as fluid + from paddle.fluid.dygraph import FC + import numpy as np + data = np.random.uniform( -1, 1, [30, 10, 32] ).astype('float32') + with fluid.dygraph.guard(): + fc = FC( "fc", 64, num_flatten_dims=2) + data = to_variable( data ) + conv = fc( data ) - # when input is single tensor - data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") - fc = fluid.FC("fc", size=1000, act="tanh") - fc_res = fc(data) - - # when input are multiple tensors - data_1 = fluid.layers.data(name="data_1", shape=[32, 32], dtype="float32") - data_2 = fluid.layers.data(name="data_2", shape=[24, 36], dtype="float32") - fc = fluid.FC("fc", size=1000, act="tanh") - fc_res = fc([data_1, data_2]) """ def __init__(self, name_scope, size, + num_flatten_dims=1, param_attr=None, bias_attr=None, - num_flatten_dims=1, - dtype=core.VarDesc.VarType.FP32, - act=None): + act=None, + is_test=False, + dtype="float32"): super(FC, self).__init__(name_scope, dtype) self._size = size @@ -881,7 +906,7 @@ class FC(layers.Layer): assert isinstance(value, Parameter) self.__w[i] = value - def build_once(self, input): + def _build_once(self, input): i = 0 for inp, param in self._helper.iter_inputs_and_params(input, self._param_attr): @@ -1022,6 +1047,8 @@ class BatchNorm(layers.Layer): or is_test to true, and the behavior is equivalent. In train mode, when setting use_global_stats True, the global mean and variance are also used during train period. + trainable_statistics(bool, Default False): Whether to calculate mean and var in eval mode. In eval mode, when + setting trainable_statistics True, mean and variance will be calculated by current batch statistics. Returns: Variable: A tensor variable which is the result after applying batch normalization on the input. @@ -1044,23 +1071,24 @@ class BatchNorm(layers.Layer): epsilon=1e-05, param_attr=None, bias_attr=None, - dtype=core.VarDesc.VarType.FP32, + dtype='float32', data_layout='NCHW', in_place=False, moving_mean_name=None, moving_variance_name=None, do_model_average_for_mean_and_var=False, fuse_with_relu=False, - use_global_stats=False): + use_global_stats=False, + trainable_statistics=False): super(BatchNorm, self).__init__(name_scope, dtype) self._param_attr = param_attr - self._param_attr = bias_attr + self._bias_attr = bias_attr self._act = act assert bias_attr is not False, "bias_attr should not be False in batch_norm." - if dtype == core.VarDesc.VarType.FP16: - self._dtype = core.VarDesc.VarType.FP32 + if dtype == "float16": + self._dtype = "float32" else: self._dtype = dtype @@ -1076,7 +1104,7 @@ class BatchNorm(layers.Layer): self._scale.stop_gradient = True self._bias = self.create_parameter( - attr=self._param_attr, + attr=self._bias_attr, shape=param_shape, dtype=self._dtype, is_bias=True) @@ -1109,8 +1137,9 @@ class BatchNorm(layers.Layer): self._is_test = is_test self._fuse_with_relu = fuse_with_relu self._use_global_stats = use_global_stats + self._trainable_statistics = trainable_statistics - def build_once(self, input): + def _build_once(self, input): pass def forward(self, input): @@ -1149,7 +1178,8 @@ class BatchNorm(layers.Layer): "is_test": self._is_test, "use_mkldnn": False, "fuse_with_relu": self._fuse_with_relu, - "use_global_stats": self._use_global_stats + "use_global_stats": self._use_global_stats, + "trainable_statistics": self._trainable_statistics }) # Currently, we don't support inplace in dygraph mode @@ -1163,22 +1193,15 @@ class Embedding(layers.Layer): This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in a lookup table. The result of this lookup is the embedding of each ID in the :attr:`input`. - - All the input variables are passed in as local variables to the LayerHelper - constructor. + All the input variables are passed in as local variables to the LayerHelper constructor Args: name_scope: See base class. - size(tuple|list): The shape of the look up table parameter. It should - have two elements which indicate the size of the dictionary of - embeddings and the size of each embedding vector respectively. + size(tuple|list): The shape of the look up table parameter. It should have two elements which indicate the size of the dictionary of embeddings and the size of each embedding vector respectively. + is_sparse(bool): The flag indicating whether to use sparse update. is_distributed(bool): Whether to run lookup table from remote parameter server. - padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup. - Otherwise the given :attr:`padding_idx` indicates padding the output - with zeros whenever lookup encounters it in :attr:`input`. If - :math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is - :math:`size[0] + dim`. + padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup. Otherwise the given :attr:`padding_idx` indicates padding the output with zeros whenever lookup encounters it in :attr:`input`. If :math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is :math:`size[0] + dim`. param_attr(ParamAttr): Parameters for this layer dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc @@ -1187,12 +1210,18 @@ class Embedding(layers.Layer): supplied inputs. Examples: + .. code-block:: python - dict_size = len(dataset.ids) - input = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32') - embedding = fluid.Embedding(size=[dict_size, 16]) - fc = embedding(input) + inp_word = np.array([[[1]]]).astype('int64') + dict_size = 20 + with fluid.dygraph.guard(): + emb = fluid.Embedding( + name_scope='embedding', + size=[dict_size, 32], + param_attr='emb.w', + is_sparse=False) + static_rlt3 = emb2(base.to_variable(inp_word)) """ def __init__(self, @@ -1242,7 +1271,13 @@ class Embedding(layers.Layer): class LayerNorm(layers.Layer): """ - ${comment} + Assume feature vectors exist on dimensions + `begin_norm_axis ... rank(input)` and calculate the moment statistics along these dimensions for each feature + vector `a` with size `H`, then normalize each feature vector using the corresponding + statistics. After that, apply learnable gain and bias on the normalized + tensor to scale and shift if `scale` and `shift` are set. + + Refer to `Layer Normalization `_ The formula is as follows: @@ -1264,7 +1299,7 @@ class LayerNorm(layers.Layer): * :math:`b`: the trainable bias parameter. Args: - input(Variable): The input tensor variable. + name_scope (str): See base class. scale(bool): Whether to learn the adaptive gain :math:`g` after normalization. Default True. shift(bool): Whether to learn the adaptive bias :math:`b` after @@ -1287,13 +1322,21 @@ class LayerNorm(layers.Layer): act(str): Activation to be applied to the output of layer normalizaiton. Default None. Returns: - ${y_comment} + Result after normalization Examples: - >>> data = fluid.layers.data(name='data', shape=[3, 32, 32], - >>> dtype='float32') - >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1) + .. code-block:: python + + import paddle.fluid as fluid + import numpy + + with fluid.dygraph.guard(): + x = numpy.random.random((3, 32, 32)).astype('float32') + layerNorm = fluid.dygraph.nn.LayerNorm( + 'LayerNorm', begin_norm_axis=1) + ret = layerNorm(fluid.dygraph.base.to_variable(x)) + """ def __init__(self, @@ -1314,7 +1357,7 @@ class LayerNorm(layers.Layer): self._bias_attr = bias_attr self._act = act - def build_once(self, input): + def _build_once(self, input): self._dtype = self._helper.input_dtype(input) input_shape = input.shape param_shape = [ @@ -1438,6 +1481,7 @@ class GRUUnit(layers.Layer): Default: 'tanh' gate_activation (string): The activation type for gates (actGate). Default: 'sigmoid' + dtype(string): The dtype of the layers Returns: tuple: The hidden value, reset-hidden value and gate values. @@ -1459,8 +1503,8 @@ class GRUUnit(layers.Layer): sigmoid=1, tanh=2, relu=3, ) - activation = activation_dict[activation] - gate_activation = activation_dict[gate_activation] + self.activation = activation_dict[activation] + self.gate_activation = activation_dict[gate_activation] self._dtype = dtype size = size // 3 @@ -1492,8 +1536,8 @@ class GRUUnit(layers.Layer): 'Hidden': updated_hidden, }, attrs={ - 'activation': 2, # tanh - 'gate_activation': 1, # sigmoid + 'activation': self.activation, + 'gate_activation': self.gate_activation, }) return updated_hidden, reset_hidden_pre, gate @@ -1501,12 +1545,15 @@ class GRUUnit(layers.Layer): class NCE(layers.Layer): """ - ${comment} + Compute and return the noise-contrastive estimation training loss. See + `Noise-contrastive estimation: A new estimation principle for unnormalized + statistical models + `_. + By default this operator uses a uniform distribution for sampling. Args: - input (Variable): input variable. - label (Variable): label. - num_total_classes (int):${num_total_classes_comment} + name_scope (str): See base class. + num_total_classes (int): Total number of classes in all samples sample_weight (Variable|None): A Variable of shape [batch_size, 1] storing a weight for each sample. The default weight for each sample is 1.0. @@ -1519,7 +1566,7 @@ class NCE(layers.Layer): If it is set to None or one attribute of ParamAttr, nce will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. - num_neg_samples (int): ${num_neg_samples_comment} + num_neg_samples (int): The number of negative classes. The default value is 10. name (str|None): A name for this layer(optional). If set None, the layer will be named automatically. Default: None. sampler (str): The sampler used to sample class from negtive classes. @@ -1538,37 +1585,45 @@ class NCE(layers.Layer): Examples: .. code-block:: python + import numpy as np + import paddle.fluid as fluid + window_size = 5 - words = [] - for i in xrange(window_size): - words.append(layers.data( - name='word_{0}'.format(i), shape=[1], dtype='int64')) - - dict_size = 10000 - label_word = int(window_size / 2) + 1 - - embs = [] - for i in xrange(window_size): - if i == label_word: - continue - - emb = layers.embedding(input=words[i], size=[dict_size, 32], - param_attr='emb.w', is_sparse=True) - embs.append(emb) - - embs = layers.concat(input=embs, axis=1) - loss = layers.nce(input=embs, label=words[label_word], - num_total_classes=dict_size, param_attr='nce.w', - bias_attr='nce.b') - - #or use custom distribution - dist = fluid.layers.assign(input=np.array([0.05,0.5,0.1,0.3,0.05]).astype("float32")) - loss = layers.nce(input=embs, label=words[label_word], - num_total_classes=5, param_attr='nce.w', - bias_attr='nce.b', - num_neg_samples=3, - sampler="custom_dist", - custom_dist=dist) + dict_size = 20 + label_word = int(window_size // 2) + 1 + inp_word = np.array([[[1]], [[2]], [[3]], [[4]], [[5]]]).astype('int64') + nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32') + + with fluid.dygraph.guard(): + words = [] + for i in range(window_size): + words.append(fluid.dygraph.base.to_variable(inp_word[i])) + + emb = fluid.Embedding( + 'embedding', + size=[dict_size, 32], + param_attr='emb.w', + is_sparse=False) + + embs3 = [] + for i in range(window_size): + if i == label_word: + continue + + emb_rlt = emb(words[i]) + embs3.append(emb_rlt) + + embs3 = fluid.layers.concat(input=embs3, axis=1) + nce = fluid.NCE('nce', + num_total_classes=dict_size, + num_neg_samples=2, + sampler="custom_dist", + custom_dist=nid_freq_arr.tolist(), + seed=1, + param_attr='nce.w', + bias_attr='nce.b') + + nce_loss3 = nce(embs3, words[label_word]) """ @@ -1676,7 +1731,7 @@ class NCE(layers.Layer): 'remote_prefetch': remote_prefetch } - def build_once(self, input, label, sample_weight=None): + def _build_once(self, input, label, sample_weight=None): assert isinstance(input, Variable) assert isinstance(label, Variable) @@ -1731,13 +1786,13 @@ class PRelu(layers.Layer): y = \max(0, x) + \\alpha * \min(0, x) Args: - x (Variable): The input tensor. - param_attr(ParamAttr|None): The parameter attribute for the learnable - weight (alpha). + name_scope (str): See base class. mode (string): The mode for weight sharing. It supports all, channel and element. all: all elements share same weight channel:elements in a channel share same weight element:each element has a weight + param_attr(ParamAttr|None): The parameter attribute for the learnable + weight (alpha). name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. @@ -1748,9 +1803,14 @@ class PRelu(layers.Layer): .. code-block:: python - x = fluid.layers.data(name="x", shape=[10,10], dtype="float32") + inp_np = np.ones([5, 200, 100, 100]).astype('float32') + with fluid.dygraph.guard(): mode = 'channel' - output = fluid.layers.prelu(x,mode) + prelu = fluid.PRelu( + 'prelu', + mode=mode, + param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0))) + dy_rlt = prelu(fluid.dygraph.base.to_variable(inp_np)) """ def __init__(self, name_scope, mode, param_attr=None): @@ -1762,7 +1822,7 @@ class PRelu(layers.Layer): raise ValueError('mode should be one of all, channel, element.') self._alpha_shape = [1] - def build_once(self, input): + def _build_once(self, input): if self._mode == 'channel': self._alpha_shape = [1, input.shape[1], 1, 1] elif self._mode == 'element': @@ -1805,8 +1865,7 @@ class BilinearTensorProduct(layers.Layer): - :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`. Args: - x (Variable): 2-D input tensor with shape [batch_size, M] - y (Variable): 2-D input tensor with shape [batch_size, N] + name_scope (str): See base class. size (int): The dimension of this layer. act (str, default None): Activation to be applied to the output of this layer. name (str, default None): The name of this layer. @@ -1822,7 +1881,16 @@ class BilinearTensorProduct(layers.Layer): Examples: .. code-block:: python - tensor = bilinear_tensor_product(x=layer1, y=layer2, size=1000) + import paddle.fluid as fluid + import numpy + + with fluid.dygraph.guard(): + layer1 = numpy.random.random((5, 5)).astype('float32') + layer2 = numpy.random.random((5, 4)).astype('float32') + bilinearTensorProduct = fluid.dygraph.nn.BilinearTensorProduct( + 'BilinearTensorProduct', size=1000) + ret = bilinearTensorProduct(fluid.dygraph.base.to_variable(layer1), + fluid.dygraph.base.to_variable(layer2)) """ def __init__(self, @@ -1840,7 +1908,7 @@ class BilinearTensorProduct(layers.Layer): self._name = name self._inputs = dict() - def build_once(self, x, y): + def _build_once(self, x, y): self._dtype = self._helper.input_dtype(x) param_shape = [self._size, x.shape[1], y.shape[1]] @@ -1932,7 +2000,7 @@ class Conv2DTranspose(layers.Layer): W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[1] ) Args: - input(Variable): The input image with [N, C, H, W] format. + name_scope (str): See base class. num_filters(int): The number of the filter. It is as same as the output image channel. output_size(int|tuple|None): The output image size. If output size is a @@ -1985,8 +2053,15 @@ class Conv2DTranspose(layers.Layer): Examples: .. code-block:: python - data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32') - conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3) + import paddle.fluid as fluid + import numpy + + with fluid.dygraph.guard(): + data = numpy.random.random((3, 32, 32)).astype('float32') + conv2DTranspose = fluid.dygraph.nn.Conv2DTranspose( + 'Conv2DTranspose', num_filters=2, filter_size=3) + ret = conv2DTranspose(fluid.dygraph.base.to_variable(data)) + """ def __init__(self, @@ -2016,7 +2091,7 @@ class Conv2DTranspose(layers.Layer): self._output_size = output_size self._op_type = 'conv2d_transpose' - def build_once(self, input): + def _build_once(self, input): input_channel = input.shape[1] if (input_channel == self._groups and self._num_filters == input_channel and not self._use_cudnn): @@ -2051,7 +2126,7 @@ class Conv2DTranspose(layers.Layer): self._filter_size = [filter_size_h, filter_size_w] else: self._filter_size = utils.convert_to_list( - self._output_size, 2, 'conv2d_transpose.filter_size') + self._filter_size, 2, 'conv2d_transpose.filter_size') if self._output_size is None: self._output_size = [] @@ -2098,7 +2173,7 @@ class SequenceConv(layers.Layer): in the input parameters to the function. Args: - input (Variable): ${x_comment} + name_scope (str): See base class. num_filters (int): number of filters. filter_size (int): the filter size (H and W). filter_stride (int): stride of the filter. @@ -2140,7 +2215,7 @@ class SequenceConv(layers.Layer): self._bias_attr = bias_attr self._param_attr = param_attr - def build_once(self, input): + def _build_once(self, input): self._dtype = self._helper.input_dtype(input) filter_shape = [self._filter_size * input.shape[1], self._num_filters] self._filter_param = self.create_parameter( @@ -2165,6 +2240,49 @@ class SequenceConv(layers.Layer): class RowConv(layers.Layer): + """ + ***Row-convolution operator*** + + The row convolution is called lookahead convolution. This operator was introduced in the following paper for DeepSpeech2: + http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf + + The main motivation is that a bidirectional RNN, useful in DeepSpeech like speech models, learns representation for a sequence by performing a + forward and a backward pass through the entire sequence. However, unlike + unidirectional RNNs, bidirectional RNNs are challenging to deploy in an online + and low-latency setting. The lookahead convolution incorporates information + from future subsequences in a computationally efficient manner to improve + unidirectional recurrent neural networks. The row convolution operator is + different from the 1D sequence convolution, and is computed as follows: + + Given an input sequence X of length t and input dimension D, and a filter (W) of size context * D. + + More details about row_conv please refer to the design document https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645 . + + Args: + name_scope (str): See base class. + future_context_size (int): Future context size. Please note, the shape + of convolution kernel is [future_context_size + 1, D]. + param_attr (ParamAttr): Attributes of parameters, including + name, initializer etc. + act (str): Non-linear activation to be applied to output variable. + + Returns: + the output(Out) is a LodTensor, which supports variable time-length input sequences. The underlying tensor in this LodTensor is a matrix with shape T x N, i.e., the same shape as X. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + import numpy + + with fluid.dygraph.guard(): + x = numpy.random.random((16)).astype('float32') + rowConv = fluid.dygraph.nn.RowConv( + 'RowConv', future_context_size=2) + ret = rowConv(fluid.dygraph.base.to_variable(x)) + + """ + def __init__(self, name_scope, future_context_size, @@ -2177,7 +2295,7 @@ class RowConv(layers.Layer): self._param_attr = param_attr self._future_context_size = future_context_size - def build_once(self, input): + def _build_once(self, input): self._dtype = self._helper.input_dtype(input) filter_shape = [self._future_context_size + 1, input.shape[1]] self._filter_param = self.create_parameter( @@ -2220,6 +2338,16 @@ class GroupNorm(layers.Layer): Returns: Variable: A tensor variable which is the result after applying group normalization on the input. + Examples: + .. code-block:: python + + import paddle.fluid as fluid + import numpy + + with fluid.dygraph.guard(): + x = numpy.random.random((8, 32, 32)).astype('float32') + groupNorm = fluid.dygraph.nn.GroupNorm('GroupNorm', groups=4) + ret = groupNorm(fluid.dygraph.base.to_variable(x)) """ @@ -2240,7 +2368,7 @@ class GroupNorm(layers.Layer): if data_layout != 'NCHW': raise ValueError("unsupported data layout:" + data_layout) - def build_once(self, input): + def _build_once(self, input): self._dtype = self._helper.input_dtype(input) param_shape = [input.shape[1]] if self._bias_attr: @@ -2287,13 +2415,70 @@ class GroupNorm(layers.Layer): class SpectralNorm(layers.Layer): + """ + **Spectral Normalization Layer** + + This layer calculates the spectral normalization value of weight parameters of + fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D + Parameters. Calculations are showed as follows. + + Step 1: + Generate vector U in shape of [H], and V in shape of [W]. + While H is the :attr:`dim` th dimension of the input weights, + and W is the product result of remaining dimensions. + + Step 2: + :attr:`power_iters` shoule be a positive interger, do following + calculations with U and V for :attr:`power_iters` rounds. + + .. math:: + + \mathbf{v} := \\frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2} + + \mathbf{u} := \\frac{\mathbf{W}^{T} \mathbf{v}}{\|\mathbf{W}^{T} \mathbf{v}\|_2} + + Step 3: + Calculate :math:`\sigma(\mathbf{W})` and normalize weight values. + + .. math:: + + \sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v} + + \mathbf{W} = \\frac{\mathbf{W}}{\sigma(\mathbf{W})} + + + Refer to `Spectral Normalization `_ . + + Args: + name_scope (str): See base class. + dim(int): The index of dimension which should be permuted to the first before reshaping Input(Weight) to matrix, it should be set as 0 if Input(Weight) is the weight of fc layer, and should be set as 1 if Input(Weight) is the weight of conv layer, default 0 + power_iters(int): number of power iterations to calculate spectral norm, default 1 + eps(float): epsilon for numerical stability in calculating norms + name (str): The name of this layer. It is optional. + + Returns: + Variable: A tensor variable of weight parameters after spectral normalization. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + import numpy + + with fluid.dygraph.guard(): + x = numpy.random.random((2, 8, 32, 32)).astype('float32') + spectralNorm = fluid.dygraph.nn.SpectralNorm('SpectralNorm', dim=1, power_iters=2) + ret = spectralNorm(fluid.dygraph.base.to_variable(x)) + + """ + def __init__(self, name_scope, dim=0, power_iters=1, eps=1e-12, name=None): super(SpectralNorm, self).__init__(name_scope) self._power_iters = power_iters self._eps = eps self._dim = dim - def build_once(self, weight): + def _build_once(self, weight): self._dtype = self._helper.input_dtype(weight) input_shape = weight.shape h = input_shape[self._dim] @@ -2330,6 +2515,44 @@ class SpectralNorm(layers.Layer): class TreeConv(layers.Layer): + """ + ***Tree-Based Convolution Operator*** + + Tree-Based Convolution is a kind of convolution based on tree structure. + Tree-Based Convolution is a part of Tree-Based Convolution Neural Network(TBCNN), + which is used to classify tree structures, such as Abstract Syntax Tree. + Tree-Based Convolution proposed a kind of data structure called continuous binary tree, + which regards multiway tree as binary tree. + The paper of Tree-Based Convolution Operator is here: https://arxiv.org/abs/1409.5718v1 + + + Args: + name_scope (str): See base class. + output_size(int): output feature width + num_filters(int): number of filters, Default 1 + max_depth(int): max depth of filters, Default 2 + act(str): activation function, Default tanh + param_attr(ParamAttr): the parameter attribute for the filters, Default None + bias_attr(ParamAttr): the parameter attribute for the bias of this layer, Default None + name(str): a name of this layer(optional). If set None, the layer will be named automatically, Default None + + Returns: + out(Variable): (Tensor) The feature vector of subtrees. The shape of the output tensor is [max_tree_node_size, output_size, num_filters]. The output tensor could be a new feature vector for next tree convolution layers + + Examples: + .. code-block:: python + import paddle.fluid as fluid + import numpy + + with fluid.dygraph.guard(): + nodes_vector = numpy.random.random((1, 10, 5)).astype('float32') + edge_set = numpy.random.random((1, 9, 2)).astype('int32') + treeConv = fluid.dygraph.nn.TreeConv( + 'TreeConv', output_size=6, num_filters=1, max_depth=2) + ret = treeConv(fluid.dygraph.base.to_variable(nodes_vector), fluid.dygraph.base.to_variable(edge_set)) + + """ + def __init__(self, name_scope, output_size, @@ -2348,7 +2571,7 @@ class TreeConv(layers.Layer): self._bias_attr = bias_attr self._param_attr = param_attr - def build_once(self, nodes_vector, edge_set): + def _build_once(self, nodes_vector, edge_set): assert isinstance(nodes_vector, Variable) assert isinstance(edge_set, Variable) self._dtype = self._helper.input_dtype(nodes_vector) @@ -2368,6 +2591,7 @@ class TreeConv(layers.Layer): is_bias=False) def forward(self, nodes_vector, edge_set): + if self._name: out = self.create_variable( name=self._name, dtype=self._dtype, persistable=False) diff --git a/python/paddle/fluid/dygraph/parallel.py b/python/paddle/fluid/dygraph/parallel.py index 44c20166b89906093e2211ed141754d8e6d0424a..37716cea14c016fca055790d4bbe65c37f058839 100644 --- a/python/paddle/fluid/dygraph/parallel.py +++ b/python/paddle/fluid/dygraph/parallel.py @@ -13,35 +13,42 @@ # limitations under the License. import os import six +import numpy as np from .. import core from . import layers +from . import parallel_helper from .. import framework - from ..layers import collective +from . import to_variable __all__ = ["prepare_context"] ParallelStrategy = core.ParallelStrategy -__parallel_ctx__clz__ = None - -def prepare_context(parallel_strategy): - global __parallel_ctx__clz__ - assert __parallel_ctx__clz__ is None, "ParallelContext can only be initialized once." - assert framework.in_dygraph_mode( - ) is True, "dygraph.parallel.prepare_context should be used with dygrahp mode." +def prepare_context(strategy=None): + if strategy is None: + strategy = ParallelStrategy() + strategy.nranks = Env().nranks + strategy.local_rank = Env().local_rank + strategy.trainer_endpoints = Env().trainer_endpoints + strategy.current_endpoint = Env().current_endpoint + if strategy.nranks < 2: + return + assert framework.in_dygraph_mode() is True,\ + "dygraph.parallel.prepare_context should be used with dygrahp mode." place = framework._current_expected_place() - assert place is not None, "dygraph.parallel.prepare_context should be used in fluid.dygraph.guard(place) guard." - + assert place is not None, \ + "dygraph.parallel.prepare_context should be used in fluid.dygraph.guard(place) guard." if isinstance(place, core.CUDAPlace): - __parallel_ctx__clz__ = core.NCCLParallelContext(parallel_strategy, - place) + parallel_helper._set_parallel_ctx( + core.NCCLParallelContext(strategy, place)) else: # TODO(Yancey1989): add Gloo Parallel Context to support CPU parallel computation assert ("Only support CUDAPlace for now.") - __parallel_ctx__clz__.init() + parallel_helper._init_parallel_ctx() + return strategy class Env(object): @@ -75,31 +82,108 @@ class Env(object): class DataParallel(layers.Layer): - def __init__(self, layers): + """ + Runs the module with data parallelism. + + Currently, DataParallel only supports to run the dynamic graph + with multi-process. The usage is: + `python -m paddle.distributed.launch --gpus 2 dynamic_graph_test.py`. + And the content of `dynamic_graph_test.py` is the code of examples. + + Examples: + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + import paddle.fluid.dygraph as dygraph + from paddle.fluid.optimizer import AdamOptimizer + from paddle.fluid.dygraph.nn import FC + from paddle.fluid.dygraph.base import to_variable + + place = fluid.CUDAPlace(0) + with fluid.dygraph.guard(place=place): + + # prepare the data parallel context + strategy=dygraph.parallel.prepare_context() + + fc_layer = FC("FC", 10, act="softmax") + adam = fluid.optimizer.AdamOptimizer() + + # make the module become the data parallelism module + fc_layer = dygraph.parallel.DataParallel(fc_layer, strategy) + + x_data = np.random.random(size=[10, 1]).astype(np.float32) + data = to_variable(x_data) + + hidden = fc_layer(data) + avg_loss = fluid.layers.mean(hidden) + + # scale the loss according to the number of trainers. + avg_loss = fc_layer.scale_loss(avg_loss) + + avg_loss.backward() + + # collect the gradients of trainers. + fc_layer.apply_collective_grads() + + adam.minimize(avg_loss) + fc_layer.clear_gradients() + + Args: + layers(Layer): The module that should be executed by data parallel. + strategy(ParallelStrategy): The strategy of data parallelism. + + Returns: + Layer: The data paralleled module. + """ + + def __init__(self, layers, strategy): super(DataParallel, self).__init__(layers.full_name() + "_data_parallel") - self._layers = layers - def build_once(self, *inputs, **kwargs): - #TODO(Yancey1989): broadcast all the paramters - pass + self._layers = layers + self._strategy = strategy def forward(self, *inputs, **kwargs): - def _collective_hook(iop): - op = framework._dygraph_tracer()._ops[iop._trace_id] - for k, v in six.iteritems(op.inputs): - for ivar in v: - g = ivar._grad_ivar() - if g: - g_var = framework.Variable( - block=self._helper.main_program.current_block(), - name=ivar._grad_name(), - stop_gradient=True, - ivar=g) - collective._allreduce(g_var, g_var, sync_mode=True) - - outs = self._layers(*inputs, **kwargs) - for _, op in six.iteritems(framework._dygraph_tracer()._ops): - # hook collective ops - op.iop.register_backward_hooks(_collective_hook, front=True) - return outs + return self._layers(*inputs, **kwargs) + + def scale_loss(self, loss): + """ + Scale the loss. In data parallel mode, the loss should be scale with + the number of trainers. If not in data parallel mode, return the loss + directly. + + Args: + loss(Layer): The loss of the current Model. + + Returns: + Layer: the scaled loss. + """ + if not self._is_data_parallel_mode(): + return loss + + loss_scale = to_variable( + np.array([self._strategy.nranks]).astype("float32")) + loss_scale.stop_gradient = True + loss = loss / loss_scale + return loss + + def apply_collective_grads(self): + """ + AllReduce the Parameters' gradient. + """ + if not self._is_data_parallel_mode(): + return + + for param in self._layers.parameters(): + # NOTE(zcd): The grad_ivar maybe no generated. + if param.trainable and param._ivar._grad_ivar(): + g_var = framework.Variable( + block=self._helper.main_program.current_block(), + name=param._ivar._grad_name(), + stop_gradient=True, + ivar=param._ivar._grad_ivar()) + collective._allreduce(g_var, g_var, sync_mode=True) + + def _is_data_parallel_mode(self): + return self._strategy.nranks > 1 diff --git a/python/paddle/fluid/dygraph/tracer.py b/python/paddle/fluid/dygraph/tracer.py index 9d2cbb4f03fdc807e1609f46eac44a0bb92af785..aea95f2f53049b343f00d3c58c5533b0aa45958b 100644 --- a/python/paddle/fluid/dygraph/tracer.py +++ b/python/paddle/fluid/dygraph/tracer.py @@ -24,9 +24,7 @@ __all__ = ['Tracer'] def release_op(op): - del framework._dygraph_tracer()._ops[op._trace_id].inputs - del framework._dygraph_tracer()._ops[op._trace_id].outputs - del framework._dygraph_tracer()._ops[op._trace_id].backward_refs + del framework._dygraph_tracer()._ops[op._trace_id] class Tracer(core.Tracer): @@ -49,74 +47,23 @@ class Tracer(core.Tracer): return list((item for name, item in six.iteritems(self._vars) if isinstance(item, framework.Parameter))) - def trace_op(self, op, inputs, outputs, stop_gradient=False): - # TODO(minqiyang): remove this line after we take apart all - # backward grads and forward variables - if self._train_mode: - op.inputs = inputs - inps = defaultdict(list) - for k, vars in six.iteritems(inputs): - if isinstance(vars, framework.Variable): - inps[k].append(vars._ivar) - elif isinstance(vars, list) or isinstance(vars, tuple): - for var in vars: - inps[k].append(var._ivar) - - op.outputs = outputs - outs = defaultdict(list) - for k, vars in six.iteritems(outputs): - if isinstance(vars, framework.Variable): - outs[k].append(vars._ivar) - elif isinstance(vars, list) or isinstance(vars, tuple): - for var in vars: - outs[k].append(var._ivar) - else: - inps = defaultdict(list) - for k, vars in six.iteritems(inputs): - if isinstance(vars, framework.Variable): - op.previous_ops.append(vars.op) - inps[k].append(vars._ivar) - elif isinstance(vars, list) or isinstance(vars, tuple): - for var in vars: - op.previous_ops.append(var.op) - inps[k].append(var._ivar) - - op.outputs = outputs - outs = defaultdict(list) - for k, vars in six.iteritems(outputs): - if isinstance(vars, framework.Variable): - vars.op = op - outs[k].append(vars._ivar) - elif isinstance(vars, list) or isinstance(vars, tuple): - for var in vars: - var.op = op - outs[k].append(var._ivar) + def _clear_ops(self): + self._ops = defaultdict() + self._trace_id = 0 + def trace_op(self, op, inputs, outputs, stop_gradient=False): # record op's trace id op.iop._trace_id = self._trace_id - backward_refs = self.trace(op.iop, inps, outs, op.attrs, - framework._current_expected_place(), - stop_gradient) + self.trace(op.iop, inputs, outputs, op.attrs, + framework._current_expected_place(), stop_gradient) if not stop_gradient and self._train_mode: self._trace_id += 1 self._ops[op.iop._trace_id] = op # register backward hooks and variables if needed - if len(backward_refs) > 0: - op.iop.register_backward_hooks(release_op) - - # TODO(minqiyang): remove all inputs and outputs after separate - # var and grad - op.backward_refs = defaultdict(list) - for k, v in six.iteritems(inputs): - if k in backward_refs: - op.backward_refs[k] = inputs[k] - - for k, v in six.iteritems(outputs): - if k in backward_refs: - op.backward_refs[k] = outputs[k] + op.iop.register_backward_hooks(release_op) def train_mode(self): self._train_mode = True diff --git a/python/paddle/fluid/evaluator.py b/python/paddle/fluid/evaluator.py index c84dd4bc4751df6cb922e13593d8a07e71a9b9d5..bde828a66910b6b7d1ba8580ba8973cd04896f6e 100644 --- a/python/paddle/fluid/evaluator.py +++ b/python/paddle/fluid/evaluator.py @@ -22,6 +22,7 @@ from .framework import Program, Variable, program_guard from . import unique_name from .layer_helper import LayerHelper from .initializer import Constant +from .layers import detection __all__ = [ 'ChunkEvaluator', @@ -374,7 +375,7 @@ class DetectionMAP(Evaluator): label = layers.concat([gt_label, gt_box], axis=1) # calculate mean average precision (mAP) of current mini-batch - map = layers.detection_map( + map = detection.detection_map( input, label, class_num, @@ -396,7 +397,7 @@ class DetectionMAP(Evaluator): self.has_state = var # calculate accumulative mAP - accum_map = layers.detection_map( + accum_map = detection.detection_map( input, label, class_num, diff --git a/python/paddle/fluid/executor.py b/python/paddle/fluid/executor.py index 063b65e8eefd6407a5b9a16930c8da129e5f7df6..bf9754ce2bf92a18e08b81b1843811b2a51382a9 100644 --- a/python/paddle/fluid/executor.py +++ b/python/paddle/fluid/executor.py @@ -247,6 +247,10 @@ def _to_name_str(var): raise TypeError(str(var) + " should be Variable or str") +def _get_strong_program_cache_key(program, feed, fetch_list): + return str(id(program)) + _get_program_cache_key(feed, fetch_list) + + def _get_program_cache_key(feed, fetch_list): feed_var_names = list(feed.keys()) fetch_var_names = list(map(_to_name_str, fetch_list)) @@ -356,17 +360,38 @@ class Executor(object): def __init__(self, place): self.place = place self.program_caches = dict() + self.ctx_caches = dict() + self.scope_caches = dict() + self.var_caches = dict() p = core.Place() p.set_place(self.place) self._default_executor = core.Executor(p) self._closed = False + def _get_var_cache(self, program_cache_key): + return self.var_caches.get(program_cache_key, None) + + def _get_scope_cache(self, program_cache_key): + return self.scope_caches.get(program_cache_key, None) + + def _get_ctx_cache(self, program_cache_key): + return self.ctx_caches.get(program_cache_key, None) + def _get_program_cache(self, program_cache_key): return self.program_caches.get(program_cache_key, None) def _add_program_cache(self, program_cache_key, program): self.program_caches[program_cache_key] = program + def _add_ctx_cache(self, ctx_cache_key, ctx): + self.ctx_caches[ctx_cache_key] = ctx + + def _add_scope_cache(self, scope_cache_key, scope): + self.scope_caches[scope_cache_key] = scope + + def _add_var_cache(self, var_cache_key, var): + self.var_caches[var_cache_key] = var + def _add_feed_fetch_ops(self, program, feed, fetch_list, feed_var_name, fetch_var_name): tmp_program = program.clone() @@ -645,6 +670,7 @@ class Executor(object): # performance. # TODO(panyx0718): executor should be able to run graph. assert program._program, "CompiledProgram is compiled from graph, can only run with_data_parallel." + # use_program_cache is not valid with CompiledProgram return self._run( program._program, self._default_executor, @@ -654,7 +680,7 @@ class Executor(object): fetch_var_name=fetch_var_name, scope=scope, return_numpy=return_numpy, - use_program_cache=use_program_cache) + use_program_cache=False) def _run(self, program, exe, feed, fetch_list, feed_var_name, fetch_var_name, scope, return_numpy, use_program_cache): @@ -677,9 +703,12 @@ class Executor(object): "Executor requires Program as its Parameter. But you passed in %s" % (type(program))) - cache_key = _get_program_cache_key(feed, fetch_list) if use_program_cache: + cache_key = _get_strong_program_cache_key(program, feed, fetch_list) cached_program = self._get_program_cache(cache_key) + cached_ctx = self._get_ctx_cache(cache_key) + cached_scope = self._get_scope_cache(cache_key) + cached_var = self._get_var_cache(cache_key) if cached_program is None: cached_program = self._add_feed_fetch_ops( program=program, @@ -688,9 +717,25 @@ class Executor(object): feed_var_name=feed_var_name, fetch_var_name=fetch_var_name) self._add_program_cache(cache_key, cached_program) + fetch_list_str = list(map(_to_name_str, fetch_list)) + cached_ctx = self._default_executor.prepare_ctx_cache( + cached_program.desc, 0, fetch_list_str, False) + cached_var = self._default_executor.create_variables( + cached_program.desc, scope, 0) + # currently, we cache program, vars, sub_scope here + # we suppose that in a life cycle of training, a user + # will not create many programs. So, here the basic + # rule of caching is to cache all unseen (program, var, scope) + # when a user use use_program_cache. + cached_scope = scope.new_scope() + self._add_ctx_cache(cache_key, cached_ctx) + self._add_var_cache(cache_key, cached_var) + self._add_scope_cache(cache_key, cached_scope) program = cached_program + ctx = cached_ctx + scope = cached_scope + var = cached_var else: - self.program_caches.pop(cache_key, None) program = self._add_feed_fetch_ops( program=program, feed=feed, @@ -699,7 +744,10 @@ class Executor(object): fetch_var_name=fetch_var_name) self._feed_data(program, feed, feed_var_name, scope) - exe.run(program.desc, scope, 0, True, True, fetch_var_name) + if not use_program_cache: + exe.run(program.desc, scope, 0, True, True, fetch_var_name) + else: + exe.run_cached_prepared_ctx(ctx, scope, False, False, False) outs = self._fetch_data(fetch_list, fetch_var_name, scope) if return_numpy: outs = as_numpy(outs) @@ -733,12 +781,23 @@ class Executor(object): assert len(fetch_list) == len(fetch_info) compiled = isinstance(program, compiler.CompiledProgram) if not compiled: - trainer = TrainerFactory()._create_trainer(program._fleet_opt) + # TODO: Need a better way to distinguish and specify different execution mode + if program._pipeline_opt: + trainer = TrainerFactory()._create_trainer( + program._pipeline_opt) + else: + trainer = TrainerFactory()._create_trainer(program._fleet_opt) trainer._set_program(program) else: - trainer = TrainerFactory()._create_trainer( - program.program._fleet_opt) + if program._pipeline_opt: + trainer = TrainerFactory()._create_trainer( + program.program._pipeline_opt) + else: + trainer = TrainerFactory()._create_trainer( + program.program._fleet_opt) trainer._set_program(program.program) + + # The following thread_num-determined logic will be deprecated if thread <= 0: if dataset.thread_num <= 0: raise RuntimeError( @@ -748,6 +807,26 @@ class Executor(object): trainer._set_thread(dataset.thread_num) else: trainer._set_thread(thread) + + # Adjust the reader size for small file num + if program._pipeline_opt: + dataset.set_thread(thread * + program._pipeline_opt["concurrency_list"][0]) + file_size = len(dataset.dataset.get_filelist()) + if file_size < thread: + thread = file_size + print( + "Pipeline: setting the pipeline num to %d is enough because there are only %d files" + % (file_size, file_size)) + if file_size < thread * program._pipeline_opt["concurrency_list"][ + 0]: + print( + "Pipeline: setting the 1st element in concurrency_list to %d is enough because there are only %d files" + % (file_size / thread, file_size)) + program._pipeline_opt["concurrency_list"][ + 0] = file_size / thread + dataset.set_thread( + program._pipeline_opt["concurrency_list"][0] * thread) trainer._set_debug(debug) trainer._set_fetch_var_and_info(fetch_list, fetch_info, print_period) return scope, trainer @@ -822,8 +901,7 @@ class Executor(object): trainer._set_infer(True) trainer._gen_trainer_desc() dataset._prepare_to_run() - if debug: - self._dump_debug_info(program=program, trainer=trainer) + self._dump_debug_info(program=program, trainer=trainer) self._default_executor.run_from_dataset(program.desc, scope, dataset.dataset, trainer._desc()) @@ -902,8 +980,7 @@ class Executor(object): print_period=print_period) trainer._gen_trainer_desc() dataset._prepare_to_run() - if debug: - self._dump_debug_info(program=program, trainer=trainer) + self._dump_debug_info(program=program, trainer=trainer) self._default_executor.run_from_dataset(program.desc, scope, dataset.dataset, trainer._desc()) diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py index 806499ca2e8b73562a79a3c60a05234c33b45fe8..7e89c4a36ec4b6e5d33d779bb5fd54b4e2946ec7 100644 --- a/python/paddle/fluid/framework.py +++ b/python/paddle/fluid/framework.py @@ -27,35 +27,11 @@ import six import numpy as np import subprocess import multiprocessing - +import sys from .. import compat as cpt from .proto import framework_pb2 -try: - if os.name == 'nt': - import sys - third_lib_path = os.path.abspath(os.path.dirname( - __file__)) + os.sep + '..' + os.sep + 'libs' - os.environ['path'] += ';' + third_lib_path - sys.path.append(third_lib_path) - - from . import core -except ImportError as e: - if os.name == 'nt': - executable_path = os.path.abspath(os.path.dirname(sys.executable)) - raise ImportError( - """NOTE: You may need to run \"set PATH=%s;%%PATH%%\" - if you encounters \"DLL load failed\" errors. If you have python - installed in other directory, replace \"%s\" with your own - directory. The original error is: \n %s""" % - (executable_path, executable_path, cpt.get_exception_message(e))) - else: - raise ImportError( - """NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\" - if you encounters \"libmkldnn.so not found\" errors. If you have python - installed in other directory, replace \"/usr/local/lib\" with your own - directory. The original error is: \n""" + cpt.get_exception_message(e)) -except Exception as e: - raise e + +from . import core from . import unique_name __all__ = [ @@ -106,7 +82,24 @@ def _current_expected_place(): def _cpu_num(): - return int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + if "CPU_NUM" not in os.environ.keys(): + sys.stderr.write( + 'The CPU_NUM is not specified, you should set CPU_NUM in ' + 'the environment variable list, i.e export CPU_NUM=1. CPU_NUM ' + 'indicates that how many CPUPlace are used in the current task.\n' + '!!! The default number of CPUPlaces is 1.\n\n') + os.environ['CPU_NUM'] = str(1) + cpu_num = os.environ.get('CPU_NUM') + return int(cpu_num) + + +def _cuda_ids(): + gpus_env = os.getenv("FLAGS_selected_gpus") + if gpus_env: + device_ids = [int(s) for s in gpus_env.split(",")] + else: + device_ids = six.moves.range(core.get_cuda_device_count()) + return device_ids def cuda_places(device_ids=None): @@ -140,11 +133,7 @@ def cuda_places(device_ids=None): assert core.is_compiled_with_cuda(), \ "Not compiled with CUDA" if device_ids is None: - gpus_env = os.getenv("FLAGS_selected_gpus") - if gpus_env: - device_ids = [int(s) for s in gpus_env.split(",")] - else: - device_ids = six.moves.range(core.get_cuda_device_count()) + device_ids = _cuda_ids() elif not isinstance(device_ids, (list, tuple)): device_ids = [device_ids] return [core.CUDAPlace(dev_id) for dev_id in device_ids] @@ -529,8 +518,14 @@ class Variable(object): new_ivar = self._ivar._copy_to(core.CPUPlace(), True) return np.array(new_ivar.value().get_tensor()) - def backward(self): - self._ivar._run_backward() + def backward(self, backward_strategy=None): + from .dygraph import BackwardStrategy + if backward_strategy is None: + backward_strategy = BackwardStrategy() + backward_strategy.sort_sum_gradient = False + + self._ivar._run_backward(backward_strategy) + _dygraph_tracer()._clear_ops() def gradient(self): new_ivar = self._ivar._grad_ivar()._copy_to(core.CPUPlace(), True) @@ -558,8 +553,9 @@ class Variable(object): """ if in_dygraph_mode(): # TODO(panyx0718): add more dygraph debug info. - return 'name %s, dtype: %s shape: %s' % (self.name, self.dtype, - self.shape) + return 'name %s, dtype: %s shape: %s %s' % ( + self.name, self.dtype, self.shape, + str(self._ivar.value().get_tensor())) assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) @@ -647,6 +643,8 @@ class Variable(object): @property def lod_level(self): # TODO(minqiyang): Support lod_level in dygraph mode + if in_dygraph_mode(): + raise Exception("Dygraph model DO NOT supprt lod") return self.desc.lod_level() @property @@ -758,10 +756,8 @@ class Variable(object): def _cloneVar(self, copy=False): if not copy: return self.block.create_var( - name=unique_name.generate(".".join(self.name)), - dtype=self.dtype, - persistable=self.persistable, - stop_gradient=self.stop_gradient, ) + name=unique_name.generate_with_ignorable_key(self.name), + dtype=self.dtype) else: return self @@ -992,12 +988,12 @@ class Operator(object): if op_maker.kOpRoleAttrName() not in op_attrs: op_attrs[op_maker.kOpRoleAttrName( - )] = self.block.program.op_role + )] = self.block.program._op_role role_var_name = op_maker.kOpRoleVarAttrName() if len(self.block.program. - op_role_var) != 0 and role_var_name not in op_attrs: - op_attrs[role_var_name] = self.block.program.op_role_var + _op_role_var) != 0 and role_var_name not in op_attrs: + op_attrs[role_var_name] = self.block.program._op_role_var if role_var_name in op_attrs and len(op_attrs[role_var_name]) == 0: del op_attrs[role_var_name] @@ -1006,7 +1002,7 @@ class Operator(object): return if type is None: raise ValueError( - "`type` to initilized an Operator can not be None.") + "`type` to initialized an Operator can not be None.") else: callstack_var_name = op_maker.kOpCreationCallstackAttrName() op_attrs[callstack_var_name] = list( @@ -1029,7 +1025,6 @@ class Operator(object): found = find_name(inputs, in_proto.name) assert found or in_proto.dispensable, "Input {} not found".format( in_proto.name) - if found: in_args = inputs[in_proto.name] if not isinstance(in_args, list): @@ -1039,13 +1034,17 @@ class Operator(object): "Input %s expects only one input, but %d are given." % (in_proto.name, len(in_args))) in_arg_names = [] - for arg in in_args: + for index, arg in enumerate(in_args): if isinstance(arg, six.string_types): in_arg_names.append(arg) elif isinstance(arg, six.binary_type): in_arg_names.append(arg.decode()) - else: + elif isinstance(arg, Variable): in_arg_names.append(cpt.to_text(arg.name)) + else: + raise ValueError( + "not suprt args type , should be[ string_type, binary_type, Varibale]" + ) self.desc.set_input(in_proto.name, in_arg_names) else: self.desc.set_input(in_proto.name, []) @@ -1380,7 +1379,9 @@ class Block(object): Examples: .. code-block:: python - cur_program = Program() + import paddle.fluid as fluid + + cur_program = fluid.Program() cur_block = cur_program.current_block() var = cur_block.create_var(name="X", shape=[-1, 23, 48], @@ -1662,13 +1663,22 @@ class Block(object): Operator: the append Operator. """ if in_dygraph_mode(): + attrs = kwargs.get("attrs", {}) + if _dygraph_tracer_._train_mode == False: + # eval mode + if ('trainable_statistics' not in attrs + ) or not attrs['trainable_statistics']: + attrs['is_test'] = True + else: + attrs['is_test'] = False + op = Operator( block=self, desc=None, type=kwargs.get("type", None), inputs=None, outputs=None, - attrs=kwargs.get("attrs", {})) + attrs=attrs) # record ops in tracer rather than blocks # @@ -2706,12 +2716,19 @@ class Program(object): A empty program. Examples: - >>> main_program = fluid.Program() - >>> startup_program = fluid.Program() - >>> with fluid.program_guard(main_program=main_program, startup_program=startup_program): - >>> fluid.layers.data(name="x", shape=[-1, 784], dtype='float32') - >>> fluid.layers.data(name="y", shape=[-1, 1], dtype='int32') - >>> fluid.layers.fc(name="fc", shape=[10], dtype='float32', act="relu") + .. code-block:: python + + import paddle.fluid as fluid + + main_program = fluid.Program() + startup_program = fluid.Program() + with fluid.program_guard(main_program=main_program, startup_program=startup_program): + x = fluid.layers.data(name="x", shape=[-1, 784], dtype='float32') + y = fluid.layers.data(name="y", shape=[-1, 1], dtype='int32') + z = fluid.layers.fc(name="fc", input=x, size=10, act="relu") + + print("main program is: {}".format(main_program)) + print("start up program is: {}".format(startup_program)) """ @@ -2721,7 +2738,7 @@ class Program(object): self.current_block_idx = 0 self._seed = 0 self._current_role = core.op_proto_and_checker_maker.OpRole.Forward - self._op_role_var = [] + self.__op_role_var = [] # for distribute training # _is_distributed = True if under distributed training @@ -2741,6 +2758,10 @@ class Program(object): # use Deep gradient comrepssion or not self._enable_dgc = False + self._nccl_comm_num = 1 + self._use_hierarchical_allreduce = False + self._hierarchical_allreduce_inter_nranks = 0 + self._hierarchical_allreduce_exter_nranks = 0 # @deprecated(the python memory optimize transpiler is deprecated) # whether the program is optimized by memory_optimize_transpiler @@ -2751,6 +2772,12 @@ class Program(object): self._fleet_opt = None self._program_config = None + # assigned if this program has been parsed by a pipeline optimizer + self._pipeline_opt = None + + # appending gradients times + self._appending_grad_times = 0 + @property def _is_mem_optimized(self): # if the program is optimized, operator input/outputs @@ -2762,7 +2789,7 @@ class Program(object): self.__is_mem_optimized = target @property - def op_role(self): + def _op_role(self): """ The operator role. In a enum {Forward, Backward, Optimize}. @@ -2771,31 +2798,27 @@ class Program(object): For example, the forward operator should be executed on every device. The backward operator should be executed on every device and the - parameter gradient of backward (use :code:`op_role_var` to get this + parameter gradient of backward (use :code:`_op_role_var` to get this variable) operator should be merged to one device. The optimization operators should be executed on only one device and broadcast the optimization result, i.e., the new parameter, to every other device. """ return self._current_role - @op_role.setter - def op_role(self, role): + @_op_role.setter + def _op_role(self, role): self._current_role = role @property - def op_role_var(self): + def _op_role_var(self): """ - The auxiliary variables for :code:`op_role` property. + The auxiliary variables for :code:`_op_role` property. - See Also: :code:`Program.op_role`'s documentation for details. + See Also: :code:`Program._op_role`'s documentation for details. Notes: This is a very low-level API. Users should not use it directly. """ - return self._op_role_var - - @op_role_var.setter - def set_op_role_var(self, var_name): - self._op_role_var = [var_name] + return self.__op_role_var @contextlib.contextmanager def _backward_role_guard(self): @@ -2824,16 +2847,16 @@ class Program(object): >>> p = p - 0.001 * g """ tmp_role = self._current_role - tmp_var = self._op_role_var + tmp_var = self.__op_role_var OpRole = core.op_proto_and_checker_maker.OpRole self._current_role = OpRole.Optimize - self._op_role_var = [ + self.__op_role_var = [ var.name if isinstance(var, Variable) else var for var in param_and_grads ] yield - self._op_role_var = tmp_var + self.__op_role_var = tmp_var self._current_role = tmp_role @signature_safe_contextmanager @@ -2858,16 +2881,16 @@ class Program(object): """ tmp_role = self._current_role - tmp_var = self._op_role_var + tmp_var = self.__op_role_var OpRole = core.op_proto_and_checker_maker.OpRole self._current_role = OpRole.LRSched if is_with_opt: self._current_role = int(OpRole.LRSched) | int(OpRole.Optimize) # TODO(typhoonzero): how to set target learning rate var - self._op_role_var = [] + self.__op_role_var = [] yield - self._op_role_var = tmp_var + self.__op_role_var = tmp_var self._current_role = tmp_role def __str__(self): @@ -2901,6 +2924,15 @@ class Program(object): ValueError: If any of required fields is not set and throw_on_error is True. + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + prog = fluid.default_main_program() + prog_string = prog.to_string(throw_on_error=True, with_details=False) + print(prog_string) + """ assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) @@ -2940,13 +2972,20 @@ class Program(object): * Set for_test to False when we want to clone the program for training. * Set for_test to True when we want to clone the program for testing. + We will not do any prune on program here, So if you just want an + forward program for testing, please use :code:`clone` before using + :code:`Opimizer.minimize` + + Notes: + 1. :code:`Program.clone()` method DOES NOT clone :code:`py_reader`. + 2. This API DOES NOT prune any operator. Use + :code:`clone(for_test=True)` before backward and optimization please. E.g. - Notes: This API DOES NOT prune any operator. Use - :code:`clone(for_test=True)` before backward and optimization please. e.g. + .. code-block:: python - >>> test_program = fluid.default_main_program().clone(for_test=True) - >>> optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9) - >>> optimizer.minimize() + test_program = fluid.default_main_program().clone(for_test=True) + optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9) + optimizer.minimize() Args: for_test(bool): True if change the :code:`is_test` attribute of @@ -2957,55 +2996,107 @@ class Program(object): Examples: - 1. To clone a test program, the sample code is: - - >>> import paddle.fluid as fluid - >>> train_program = fluid.Program() - >>> startup_program = fluid.Program() - >>> with fluid.program_guard(train_program, startup_program): - >>> img = fluid.layers.data(name='image', shape=[784]) - >>> hidden = fluid.layers.fc(input=img, size=200, act='relu') - >>> hidden = fluid.layers.dropout(hidden, dropout_prob=0.5) - >>> loss = fluid.layers.cross_entropy( - >>> input=fluid.layers.fc(hidden, size=10, act='softmax'), - >>> label=fluid.layers.data(name='label', shape=[1], dtype='int64')) - >>> - >>> test_program = train_program.clone(for_test=True) - >>> - >>> sgd = fluid.optimizer.SGD(learning_rate=1e-3) - >>> with fluid.program_guard(train_program, startup_program): - >>> sgd.minimize(loss) - - 2. The :code:`clone` method can be avoid if you create program for - training and program for testing individually. - - >>> import paddle.fluid as fluid - >>> - >>> def network(is_test): - >>> img = fluid.layers.data(name='image', shape=[784]) - >>> hidden = fluid.layers.fc(input=img, size=200, act='relu') - >>> hidden = fluid.layers.dropout(hidden, dropout_prob=0.5, is_test=is_test) - >>> loss = fluid.layers.cross_entropy( - >>> input=fluid.layers.fc(hidden, size=10, act='softmax'), - >>> label=fluid.layers.data(name='label', shape=[1], dtype='int64')) - >>> return loss - >>> - >>> train_program = fluid.Program() - >>> startup_program = fluid.Program() - >>> test_program = fluid.Program() - >>> - >>> with fluid.program_guard(train_program, startup_program): - >>> with fluid.unique_name.guard(): - >>> loss = network(is_test=False) - >>> sgd = fluid.optimizer.SGD(learning_rate=1e-3) - >>> sgd.minimize(loss) - >>> - >>> # the test startup program is not used. - >>> with fluid.program_guard(test_program, fluid.Program()): - >>> with fluid.unique_name.guard(): - >>> loss = network(is_test=True) - - The two code snippets above will generate same programs. + Notes: The Program Descs' order maybe different after :code:`clone` and + this will not affect your training or testing progress. In the following + example we give you an simple method :code:`print_prog(program)` to + print Program Descs inorder to make sure you have same print result + after :code:`clone`: + + .. code-block:: python + + import paddle.fluid as fluid + import six + + + def print_prog(prog): + for name, value in sorted(six.iteritems(prog.block(0).vars)): + print(value) + for op in prog.block(0).ops: + print("op type is {}".format(op.type)) + print("op inputs are {}".format(op.input_arg_names)) + print("op outputs are {}".format(op.output_arg_names)) + for key, value in sorted(six.iteritems(op.all_attrs())): + if key not in ['op_callstack', 'op_role_var']: + print(" [ attrs: {}: {} ]".format(key, value)) + + + 1. To clone a test program, the sample code is: + .. code-block:: python + + import paddle.fluid as fluid + import six + + def print_prog(prog): + for name, value in sorted(six.iteritems(prog.block(0).vars)): + print(value) + for op in prog.block(0).ops: + print("op type is {}".format(op.type)) + print("op inputs are {}".format(op.input_arg_names)) + print("op outputs are {}".format(op.output_arg_names)) + for key, value in sorted(six.iteritems(op.all_attrs())): + if key not in ['op_callstack', 'op_role_var']: + print(" [ attrs: {}: {} ]".format(key, value)) + + train_program = fluid.Program() + startup_program = fluid.Program() + with fluid.program_guard(train_program, startup_program): + with fluid.unique_name.guard(): + img = fluid.layers.data(name='image', shape=[784]) + hidden = fluid.layers.fc(input=img, size=200, act='relu') + hidden = fluid.layers.dropout(hidden, dropout_prob=0.5) + loss = fluid.layers.cross_entropy( + input=fluid.layers.fc(hidden, size=10, act='softmax'), + label=fluid.layers.data(name='label', shape=[1], dtype='int64')) + avg_loss = fluid.layers.mean(loss) + test_program = train_program.clone(for_test=False) + print_prog(test_program) + with fluid.program_guard(train_program, startup_program): + with fluid.unique_name.guard(): + sgd = fluid.optimizer.SGD(learning_rate=1e-3) + sgd.minimize(avg_loss) + + + 2. The clone method can be avoid if you create program for training and program for testing individually. + .. code-block:: python + + import paddle.fluid as fluid + import six + + def print_prog(prog): + for name, value in sorted(six.iteritems(prog.block(0).vars)): + print(value) + for op in prog.block(0).ops: + print("op type is {}".format(op.type)) + print("op inputs are {}".format(op.input_arg_names)) + print("op outputs are {}".format(op.output_arg_names)) + for key, value in sorted(six.iteritems(op.all_attrs())): + if key not in ['op_callstack', 'op_role_var']: + print(" [ attrs: {}: {} ]".format(key, value)) + def network(is_test): + img = fluid.layers.data(name='image', shape=[784]) + hidden = fluid.layers.fc(input=img, size=200, act='relu') + hidden = fluid.layers.dropout(hidden, dropout_prob=0.5) + loss = fluid.layers.cross_entropy( + input=fluid.layers.fc(hidden, size=10, act='softmax'), + label=fluid.layers.data(name='label', shape=[1], dtype='int64')) + avg_loss = fluid.layers.mean(loss) + return avg_loss + + + train_program_2 = fluid.Program() + startup_program_2 = fluid.Program() + test_program_2 = fluid.Program() + with fluid.program_guard(train_program_2, startup_program_2): + with fluid.unique_name.guard(): + sgd = fluid.optimizer.SGD(learning_rate=1e-3) + sgd.minimize(avg_loss) + # the test startup program is not used. + with fluid.program_guard(test_program_2, fluid.Program()): + with fluid.unique_name.guard(): + loss = network(is_test=True) + print(test_program_2) + + The two code snippets above will generate and print same programs. """ if for_test: p = self._inference_optimize(prune_read_op=False) @@ -3019,7 +3110,8 @@ class Program(object): ] p._current_role = self._current_role - p._op_role_var = self._op_role_var + p.__op_role_var = self.__op_role_var + p._appending_grad_times = self._appending_grad_times p._sync_with_cpp() @@ -3175,6 +3267,17 @@ class Program(object): the random seed from random device. Notes: It must be set before the operators have been added. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + prog = fluid.default_main_program() + random_seed = prog.random_seed + print(random_seed) + prog.random_seed = 1 + print(prog.random_seed) """ return self._seed @@ -3182,6 +3285,15 @@ class Program(object): def num_blocks(self): """ The number of blocks in this program. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + prog = fluid.default_main_program() + num_blocks = prog.num_blocks + print(num_blocks) """ return self.desc.num_blocks() @@ -3197,6 +3309,15 @@ class Program(object): def global_block(self): """ Get the first block of this program. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + prog = fluid.default_main_program() + gb_block = prog.global_block() + print(gb_block) """ return self.blocks[0] @@ -3208,6 +3329,15 @@ class Program(object): Returns: Block: The :code:`index` block + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + prog = fluid.default_main_program() + block_0 = prog.block(0) + print(block_0) """ return self.blocks[index] @@ -3215,6 +3345,15 @@ class Program(object): """ Get the current block. The :code:`current` block is the block to append operators. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + prog = fluid.default_main_program() + current_blk = prog.current_block() + print(current_blk) """ return self.blocks[self.current_block_idx] @@ -3333,6 +3472,17 @@ class Program(object): Returns: iterable: The generator will yield every variable in this program. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + prog = fluid.default_main_program() + img = fluid.layers.data(name='img', shape=[1,28,28], dtype='float32') + label = fluid.layers.data(name='label', shape=[128,1], dtype='int64') + for var in prog.list_vars(): + print(var) """ for each_block in self.blocks: for each_var in list(each_block.vars.values()): @@ -3401,6 +3551,15 @@ class Parameter(Variable): Returns(str): The debug string. + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + prog = fluid.default_main_program() + rlt = fluid.layers.data("fake_data", shape=[1,1], dtype='float32') + debug_str = prog.to_string(throw_on_error=True, with_details=False) + print(debug_str) """ assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) @@ -3437,6 +3596,21 @@ def default_startup_program(): Returns: Program: startup program + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + main_program = fluid.Program() + startup_program = fluid.Program() + with fluid.program_guard(main_program=main_program, startup_program=startup_program): + x = fluid.layers.data(name="x", shape=[-1, 784], dtype='float32') + y = fluid.layers.data(name="y", shape=[-1, 1], dtype='int32') + z = fluid.layers.fc(name="fc", input=x, size=10, act="relu") + + print("main program is: {}".format(fluid.default_main_program())) + print("start up program is: {}".format(fluid.default_startup_program())) """ return _startup_program_ @@ -3455,6 +3629,35 @@ def default_main_program(): Returns: Program: main program + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + # Sample Network: + data = fluid.layers.data(name='image', shape=[3, 224, 224], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') + + conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None) + bn1 = fluid.layers.batch_norm(conv1, act='relu') + pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2) + conv2 = fluid.layers.conv2d(pool1, 16, 5, 1, act=None) + bn2 = fluid.layers.batch_norm(conv2, act='relu') + pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2) + + fc1 = fluid.layers.fc(pool2, size=50, act='relu') + fc2 = fluid.layers.fc(fc1, size=102, act='softmax') + + loss = fluid.layers.cross_entropy(input=fc2, label=label) + loss = fluid.layers.mean(loss) + opt = fluid.optimizer.Momentum( + learning_rate=0.1, + momentum=0.9, + regularization=fluid.regularizer.L2Decay(1e-4)) + opt.minimize(loss) + + print(fluid.default_main_program()) """ return _main_program_ @@ -3493,8 +3696,8 @@ def switch_startup_program(program): @signature_safe_contextmanager def program_guard(main_program, startup_program=None): """ - Change the global main program and startup program with `with` statement. - Layer functions in the Python `with` block will append operators and + Change the global main program and startup program with `"with"` statement. + Layer functions in the Python `"with"` block will append operators and variables to the new main programs. Examples: @@ -3522,9 +3725,9 @@ def program_guard(main_program, startup_program=None): data = fluid.layers.data(name='image', shape=[784, 784], dtype='float32') Args: - main_program(Program): New main program inside `with` statement. - startup_program(Program): New startup program inside `with` statement. - None means do not change startup program. + main_program(Program): New main program inside `"with"` statement. + startup_program(Program): New startup program inside `"with"` statement. + None means not changing startup program. """ if not isinstance(main_program, Program): raise TypeError("main_program should be Program") diff --git a/python/paddle/fluid/incubate/fleet/base/fleet_base.py b/python/paddle/fluid/incubate/fleet/base/fleet_base.py index f2f72b0f505fd43607f5104e39f5167f55fa432e..acabec3e82aa50e07753bd8735406e0b378dabee 100644 --- a/python/paddle/fluid/incubate/fleet/base/fleet_base.py +++ b/python/paddle/fluid/incubate/fleet/base/fleet_base.py @@ -16,22 +16,21 @@ from __future__ import print_function import abc -from enum import Enum - -from paddle.fluid.optimizer import SGD +import paddle.fluid as fluid from paddle.fluid.executor import Executor +from paddle.fluid.optimizer import SGD -from role_maker import RoleMakerBase -from role_maker import MPISymetricRoleMaker -from role_maker import UserDefinedRoleMaker +from paddle.fluid.incubate.fleet.base.role_maker import MPISymetricRoleMaker +from paddle.fluid.incubate.fleet.base.role_maker import RoleMakerBase +from paddle.fluid.incubate.fleet.base.role_maker import UserDefinedRoleMaker -class Mode(Enum): +class Mode: """ There are various mode for fleet, each of them is designed for different model. """ - TRANSPILER = 1, - PSLIB = 2, + TRANSPILER = 1 + PSLIB = 2 COLLECTIVE = 3 @@ -48,7 +47,6 @@ class Fleet(object): __metaclass__ = abc.ABCMeta def __init__(self, mode): - assert isinstance(mode, Mode) self._is_initialized = False self._mode = mode self._optimizer = None @@ -79,9 +77,9 @@ class Fleet(object): Get current total worker number. Returns: - int: worker number + int: worker numbers """ - return len(self._role_maker.get_trainer_endpoints()) + return self._role_maker.worker_num() def is_worker(self): """ @@ -173,36 +171,25 @@ class Fleet(object): end += length return files[start:end] - def init(self, executor, role_maker=None): + def init(self, role_maker=None): """ should be called only once in user's python scripts, init() will initialize RoleMaker which is used for identifying current node's role, e.g. worker, server, etc. Args: - executor(Executor): The executor to run fleet. role_maker(RoleMakerBase): subclass of RoleMakerBase. Returns: None """ - if not isinstance(executor, Executor): - raise ValueError("executor must be an instance of Executor") + self._executor = Executor(fluid.CPUPlace()) if role_maker and not isinstance(role_maker, RoleMakerBase): raise ValueError("role_maker must be an instance of RoleMakerBase") - if isinstance(role_maker, MPISymetricRoleMaker): - self._role_maker = role_maker - self._role_maker.generate_role() - - elif isinstance(role_maker, UserDefinedRoleMaker): - self._role_maker = role_maker - - else: - raise ValueError( - "role_maker must be an instance of UserDefinedRoleMaker/MPISymetricRoleMaker" - ) + self._role_maker = role_maker + self._role_maker.generate_role() self._is_initialized = True @@ -215,23 +202,20 @@ class Fleet(object): pass @abc.abstractmethod - def run_server(self, ): + def run_server(self): pass @abc.abstractmethod def stop_worker(self): pass - @abc.abstractmethod - def stop(self): - pass - @abc.abstractmethod def distributed_optimizer(self, optimizer, strategy=None): pass @abc.abstractmethod def save_inference_model(self, + executor, dirname, feeded_var_names, target_vars, @@ -240,7 +224,7 @@ class Fleet(object): pass @abc.abstractmethod - def save_persistables(self, dirname, main_program=None): + def save_persistables(self, executor, dirname, main_program=None): pass diff --git a/python/paddle/fluid/incubate/fleet/base/role_maker.py b/python/paddle/fluid/incubate/fleet/base/role_maker.py index 5371252213b2624ca44bb54b20a385b306967f8e..ae6768f8f568f6877c591134d9766d6542f956e7 100644 --- a/python/paddle/fluid/incubate/fleet/base/role_maker.py +++ b/python/paddle/fluid/incubate/fleet/base/role_maker.py @@ -13,15 +13,15 @@ # limitations under the License. from __future__ import print_function -from enum import Enum __all__ = [ - 'Role', 'RoleMakerBase', 'MPISymetricRoleMaker', 'UserDefinedRoleMaker' + 'Role', 'RoleMakerBase', 'MPISymetricRoleMaker', 'UserDefinedRoleMaker', + 'UserDefinedCollectiveRoleMaker', 'PaddleCloudRoleMaker' ] -class Role(Enum): - WORKER = 1, +class Role: + WORKER = 1 SERVER = 2 @@ -61,6 +61,15 @@ class RoleMakerBase(object): """ raise NotImplementedError("Please implement this method in child class") + def worker_num(self): + """ + Get current total worker number. + + Returns: + int: worker number + """ + raise NotImplementedError("Please implement this method in child class") + def worker_index(self): """ Get current worker id. @@ -197,6 +206,9 @@ class MPISymetricRoleMaker(MPIRoleMaker): return self.is_worker() and 0 == self.worker_index() return False + def worker_num(self): + return self._worker_num() + def is_worker(self): """ return whether current process is worker assigned by role maker @@ -269,8 +281,8 @@ class MPISymetricRoleMaker(MPIRoleMaker): """ if not self._role_is_generated: # TODO(guru4elephant): only allow to be called once - self._worker_endpoints = self._get_ips() - self._server_endpoints = self._get_ips() + self._worker_endpoints = self._get_ips()[1::2] + self._server_endpoints = self._get_ips()[::2] if 0 == self._get_rank() % self._proc_per_node % 2: self._node_type = 0 @@ -280,6 +292,50 @@ class MPISymetricRoleMaker(MPIRoleMaker): self._role_is_generated = True +class PaddleCloudRoleMaker(RoleMakerBase): + def __init__(self): + super(PaddleCloudRoleMaker, self).__init__() + + def generate_role(self): + if not self._role_is_generated: + self.port = os.getenv("PADDLE_PORT", "6174") + self.pserver_ips = os.getenv("PADDLE_PSERVERS", "") + eplist = [] + for ip in pserver_ips.split(","): + eplist.append(':'.join([ip, port])) + self.endpoints = ",".join(eplist) + self.trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "1")) + self.current_endpoint = os.getenv("POD_IP", + "localhost") + ":" + port + self.role = os.getenv("TRAINING_ROLE", "TRAINER") + self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0")) + self.eplist = eplist + self.endpoints = self.endpoints.split(",") + if self.role.upper() == "PSERVER": + self.current_id = self.endpoints.index(self.current_endpoint) + else: + self.current_id = self.trainer_id + self._role_is_generated = True + + def is_wokrer(self): + return self._role == Role.WORKER + + def is_server(self): + return self._role == Role.SERVER + + def is_first_worker(self): + return self._role == Role.WORKER and self._current_id == 0 + + def worker_index(self): + return self._current_id + + def server_index(self): + return self._current_id + + def worker_num(self): + return self._worker_num + + class UserDefinedRoleMaker(RoleMakerBase): def __init__(self, current_id=0, @@ -293,10 +349,32 @@ class UserDefinedRoleMaker(RoleMakerBase): """ super(UserDefinedRoleMaker, self).__init__() - self._current_id = current_id - self._role = role - self._worker_num = worker_num - self._server_endpoints = server_endpoints + if not isinstance(current_id, int): + raise TypeError("current_id must be as int") + else: + if current_id < 0: + raise ValueError("current_id must be gather or equal 0") + self._current_id = current_id + + if role != Role.WORKER and role != Role.SERVER: + raise TypeError("role must be as Role") + else: + self._role = role + + if not isinstance(worker_num, int): + raise TypeError("worker_num must be as int") + else: + if worker_num < 0: + raise ValueError("worker_num must be gather or equal 0") + self._worker_num = worker_num + + if not isinstance(server_endpoints, list): + raise TypeError("server_endpoints must be as string list") + else: + self._server_endpoints = server_endpoints + + def generate_role(self): + self._role_is_generated = True def is_worker(self): return self._role == Role.WORKER @@ -312,3 +390,43 @@ class UserDefinedRoleMaker(RoleMakerBase): def server_index(self): return self._current_id + + def worker_num(self): + return self._worker_num + + +class UserDefinedCollectiveRoleMaker(RoleMakerBase): + def __init__(self, current_id=0, worker_endpoints=None): + """ + UserDefinedCollectiveRoleMaker is designed for worker assignment + under manual for collective mode. + """ + super(UserDefinedCollectiveRoleMaker, self).__init__() + + if not isinstance(current_id, int): + raise TypeError("current_id must be as int") + else: + if current_id < 0: + raise ValueError("current_id must be greater or equal 0") + self._current_id = current_id + + if not isinstance(worker_endpoints, list): + raise TypeError("worker_endpoints must be as string list") + else: + self._worker_endpoints = worker_endpoints + self._worker_num = len(self._worker_endpoints) + + def generate_role(self): + self._role_is_generated = True + + def is_worker(self): + return True + + def is_first_worker(self): + return self._current_id == 0 + + def worker_index(self): + return self._current_id + + def worker_num(self): + return self._worker_num diff --git a/python/paddle/fluid/incubate/fleet/collective/__init__.py b/python/paddle/fluid/incubate/fleet/collective/__init__.py index e381a0d8c7124b8e9dd099ef0d99faa6985a8548..100474244c5c8b16ffd3108e2b5e2e478c433bfa 100644 --- a/python/paddle/fluid/incubate/fleet/collective/__init__.py +++ b/python/paddle/fluid/incubate/fleet/collective/__init__.py @@ -17,9 +17,9 @@ import paddle.fluid as fluid import paddle.fluid.io as io import paddle.fluid.transpiler.distribute_transpiler as dist_transpiler -from ..base.fleet_base import Fleet -from ..base.fleet_base import Mode -from ..base.fleet_base import DistributedOptimizer +from paddle.fluid.incubate.fleet.base.fleet_base import Fleet +from paddle.fluid.incubate.fleet.base.fleet_base import Mode +from paddle.fluid.incubate.fleet.base.fleet_base import DistributedOptimizer class Collective(Fleet): @@ -47,17 +47,12 @@ class Collective(Fleet): logging.warn( "You should not call 'stop_worker' method for collective mode.") - def stop(self): - """ - stop(): will be called after a user finishes his/her training task. - """ - logging.warn("You should not call 'stop' method for collective mode.") - def distributed_optimizer(self, optimizer, strategy=None): self._optimizer = CollectiveOptimizer(optimizer, strategy) return self._optimizer def save_inference_model(self, + executor, dirname, feeded_var_names=None, target_vars=None, @@ -67,7 +62,7 @@ class Collective(Fleet): self._executor, main_program, None, None, export_for_deployment) - def save_persistables(self, dirname, main_program=None): + def save_persistables(self, executor, dirname, main_program=None): io.save_persistables(self._executor, dirname, main_program, None) diff --git a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/__init__.py b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/__init__.py index ec066187c238815a5b262fb752d10ad6a5730cbe..2111831b9fa2e4566542f6928db627e423504320 100644 --- a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/__init__.py +++ b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/__init__.py @@ -12,16 +12,16 @@ # See the License for the specific language governing permissions and import sys -from .optimizer_factory import * +from optimizer_factory import * from google.protobuf import text_format import paddle.fluid as fluid from paddle.fluid.framework import Program -from ...base.fleet_base import Fleet -from ...base.fleet_base import Mode -from ...base.role_maker import MPISymetricRoleMaker -from ...base.fleet_base import DistributedOptimizer +from paddle.fluid.incubate.fleet.base.fleet_base import Fleet +from paddle.fluid.incubate.fleet.base.fleet_base import Mode +from paddle.fluid.incubate.fleet.base.fleet_base import DistributedOptimizer +from paddle.fluid.incubate.fleet.base.role_maker import MPISymetricRoleMaker class PSLib(Fleet): @@ -33,8 +33,8 @@ class PSLib(Fleet): self._main_programs = [] self._scopes = [] - def init(self, executor, role_maker=None): - super(PSLib, self).init(executor, MPISymetricRoleMaker()) + def init(self, role_maker=None): + super(PSLib, self).init(MPISymetricRoleMaker()) self._fleet_ptr = fluid.core.Fleet() def init_worker(self): @@ -106,14 +106,33 @@ class PSLib(Fleet): raise NameError( "You should run DistributedOptimizer.minimize() first") - def init_server(self, model_dir=None): - pass + def init_server(self, model_dir=None, **kwargs): + """ + init_server() will be called by user. It will load model from model_dir. + + Args: + model_dir(str): load model path, can be local or hdfs/afs path. + kwargs: user-defined attributes, currently support following: + model(int): load model mode. + 0 is for load whole model, + 1 is for load delta model (load diff), + default is 0. + + Example: + >>> fleet.init_server("/you/path/to/model", mode = 0) + + """ + mode = kwargs.get("mode", 0) + self._role_maker._barrier_worker() + if self._role_maker.is_first_worker(): + self._fleet_ptr.load_model(model_dir, mode) + self._role_maker._barrier_worker() def run_server(self): """ init_pserver(): will be called by user. When a user knows current process is_worker(), he/she should call init_pserver() to initialize global information about parameter server - """ + """ if self._opt_info: if "fleet_desc" in self._opt_info: self._dist_desc_str = text_format.MessageToString( @@ -150,23 +169,12 @@ class PSLib(Fleet): self._role_maker._barrier_all() self._role_maker._finalize() - def stop(self): - """ - stop(): will be called after a user finishes his/her training task. Fleet instance will be - destroyed when stop() is called. - """ - self._role_maker._barrier_worker() - if self._role_maker.is_first_worker(): - self._fleet_ptr.stop_server() - self._role_maker._barrier_worker() - self._role_maker._barrier_all() - self._role_maker._finalize() - - def distributed_optimizer(self, optimizer, strategy=None): + def distributed_optimizer(self, optimizer, strategy={}): self._optimizer = DownpourOptimizer(optimizer, strategy) return self._optimizer def save_inference_model(self, + executor, dirname, feeded_var_names=None, target_vars=None, @@ -177,8 +185,81 @@ class PSLib(Fleet): """ self._fleet_ptr.save_model(dirname) - def save_persistables(self, dirname, main_program=None): - self._fleet_ptr.save_model(dirname) + def save_persistables(self, executor, dirname, main_program=None, **kwargs): + """ + save presistable parameters, + when using fleet, it will save sparse and dense feature + + Args: + dirname(str): save path. It can be hdfs/afs path or local path + main_program(Program): fluid program, default None + kwargs: use define property, current support following + mode(int): 0 means save all pserver model, + 1 means save delta pserver model (save diff), + 2 means save xbox base, + 3 means save batch model. + + Example: + >>> fleet.save_persistables(dirname="/you/path/to/model", mode = 0) + + """ + mode = kwargs.get("mode", 0) + self._fleet_ptr.client_flush() + self._role_maker._barrier_worker() + if self._role_maker.is_first_worker(): + self._fleet_ptr.save_model(dirname, mode) + self._role_maker._barrier_worker() + + def shrink_sparse_table(self): + """ + shrink cvm of all sparse embedding in pserver, the decay rate + is defined as "show_click_decay_rate" in fleet_desc.prototxt + + Example: + >>> fleet.shrink_sparse_table() + + """ + self._role_maker._barrier_worker() + if self._role_maker.is_first_worker(): + for i in self._opt_info["fleet_desc"].trainer_param.sparse_table: + self._fleet_ptr.shrink_sparse_table(i.table_id) + self._role_maker._barrier_worker() + + def shrink_dense_table(self, decay, scope=None, table_id=None): + """ + shrink all dense params in pserver by multiplying by decay + + Args: + decay(float): the decay rate, usually range in (0, 1) + scope(Scope): Scope object, default is fluid.global_scope() + table_id(int): table id of shrinking dense table. None means shrink all, + you should specify it when using multiple scopes, + default is None. + + Example: + >>> fleet.shrink_dense_table(0.98, myscope1, 1) + >>> fleet.shrink_dense_table(0.98, myscope1, 2) + >>> fleet.shrink_dense_table(0.98, myscope2, 3) + + """ + if scope is None: + scope = fluid.global_scope() + self._role_maker._barrier_worker() + if self._role_maker.is_first_worker(): + for i in self._opt_info["fleet_desc"].trainer_param.dense_table: + if table_id is not None and table_id != i.table_id: + continue + var_list = [var for var in i.dense_variable_name] + skip = False + for var in var_list: + if scope.find_var(var) is None: + skip = True + break + if skip: + continue + self._fleet_ptr.shrink_dense_table(i.table_id, scope, var_list, + decay) + self._role_maker._barrier_worker() def _set_opt_info(self, opt_info): """ @@ -273,7 +354,8 @@ class DownpourOptimizer(DistributedOptimizer): losses, startup_programs, parameter_list, - no_grad_set) + no_grad_set, + self._strategy) fleet._set_opt_info(opt_info) diff --git a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/node.py b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/node.py index 7a1925a95fd29259c137bc592aff653554381ada..ed6ca5db49d7f6840f62b1ae15f60cce58769f4b 100644 --- a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/node.py +++ b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/node.py @@ -62,10 +62,19 @@ class DownpourServer(Server): Returns: return None """ + for table in self._server.downpour_server_param.downpour_table_param: + if table.table_id == table_id: + if table.type == pslib.PS_SPARSE_TABLE: + return + else: + raise ValueError("expect table %s type=%s, but actual type=%s" \ + %(table_id, pslib.PS_SPARSE_TABLE, table.type)) table = self._server.downpour_server_param.downpour_table_param.add() table.table_id = table_id table.table_class = "DownpourSparseTable" table.type = pslib.PS_SPARSE_TABLE + table.compress_in_save = True + table.shard_num = 1000 table.accessor.accessor_class = "DownpourFeatureValueAccessor" table.accessor.sparse_sgd_param.learning_rate = learning_rate table.accessor.sparse_sgd_param.initial_g2sum = 3 @@ -94,10 +103,24 @@ class DownpourServer(Server): Returns: return None """ + fea_dim = 0 + for param in filter(lambda x: x.name.find("embedding") == -1, + param_var): + fea_dim += reduce(lambda x, y: x * y, param.shape, 1) + + for table in self._server.downpour_server_param.downpour_table_param: + if table.table_id == table_id: + if table.type == pslib.PS_DENSE_TABLE: + table.accessor.fea_dim = fea_dim + return + else: + raise ValueError("expect table %s type=%s, but actual type=%s" \ + %(table_id, pslib.PS_DENSE_TABLE, table.type)) table = self._server.downpour_server_param.downpour_table_param.add() table.table_id = table_id table.table_class = "DownpourDenseTable" table.type = pslib.PS_DENSE_TABLE + table.compress_in_save = True table.accessor.accessor_class = "DownpourDenseValueAccessor" table.accessor.dense_sgd_param.name = "adam" table.accessor.dense_sgd_param.adam.learning_rate = learning_rate @@ -106,10 +129,6 @@ class DownpourServer(Server): table.accessor.dense_sgd_param.adam.ada_epsilon = 1e-8 table.accessor.dense_sgd_param.adam.mom_decay_rate = 0.99 table.accessor.dense_sgd_param.naive.learning_rate = 0.0002 - fea_dim = 0 - for param in filter(lambda x: x.name.find("embedding") == -1, - param_var): - fea_dim += reduce(lambda x, y: x * y, param.shape, 1) table.accessor.fea_dim = fea_dim def add_data_norm_table(self, table_id, learning_rate, param_var, grad_var): @@ -123,17 +142,27 @@ class DownpourServer(Server): Returns: return None """ + fea_dim = 0 + for param in filter(lambda x: x.name.find("embedding") == -1, + param_var): + fea_dim += reduce(lambda x, y: x * y, param.shape, 1) + + for table in self._server.downpour_server_param.downpour_table_param: + if table.table_id == table_id: + if table.type == pslib.PS_DENSE_TABLE: + table.accessor.fea_dim = fea_dim + return + else: + raise ValueError("expect table %s type=%s, but actual type=%s" \ + %(table_id, pslib.PS_DENSE_TABLE, table.type)) table = self._server.downpour_server_param.downpour_table_param.add() table.table_id = table_id table.table_class = "DownpourDenseTable" table.type = pslib.PS_DENSE_TABLE + table.compress_in_save = True table.accessor.accessor_class = "DownpourDenseValueAccessor" table.accessor.dense_sgd_param.name = "summary" table.accessor.dense_sgd_param.summary.summary_decay_rate = 0.999999 - fea_dim = 0 - for param in filter(lambda x: x.name.find("embedding") == -1, - param_var): - fea_dim += reduce(lambda x, y: x * y, param.shape, 1) table.accessor.fea_dim = fea_dim def get_desc(self): @@ -169,6 +198,9 @@ class DownpourWorker(Worker): Returns: return None """ + for table in self._worker.sparse_table: + if table.table_id == table_id: + return table = self._worker.sparse_table.add() table.table_id = table_id table.slot_key.extend([var.name for var in slot_key_vars]) @@ -187,6 +219,9 @@ class DownpourWorker(Worker): Returns: return None """ + for table in self._worker.dense_table: + if table.table_id == table_id: + return table = self._worker.dense_table.add() table.table_id = table_id table.dense_variable_name.extend( diff --git a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py index 31f964a0e341cf0a4f1bc551f3bea1a6a47d108e..895fb6889ccb0262145d5a87de4fb4a5c579a38a 100644 --- a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py +++ b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py @@ -52,7 +52,8 @@ class DistributedAdam(DistributedOptimizerImplBase): losses, startup_program=None, parameter_list=None, - no_grad_set=None): + no_grad_set=None, + strategy={}): """ DownpounSGD is a distributed optimizer so that user can call minimize to generate backward @@ -63,6 +64,7 @@ class DistributedAdam(DistributedOptimizerImplBase): parameter_list(str list): parameter names defined by users no_grad_set(set): a set of variables that is defined by users so that these variables do not need gradient computation + strategy(dict): user-defined properties Returns: [optimize_ops, grads_and_weights] """ @@ -76,6 +78,15 @@ class DistributedAdam(DistributedOptimizerImplBase): ps_param = pslib.PSParameter() server = DownpourServer() worker = DownpourWorker(self._window) + # if user specify a fleet_desc.prototxt file, then load the file + # instead of creating default fleet_desc.prototxt. + # user can specify server_param or trainer_param or fs_client_param. + if strategy.get("fleet_desc_file") is not None: + fleet_desc_file = strategy["fleet_desc_file"] + with open(fleet_desc_file) as f: + text_format.Merge(f.read(), ps_param) + server.get_desc().CopyFrom(ps_param.server_param) + worker.get_desc().CopyFrom(ps_param.trainer_param) sparse_table_index = 0 server.add_sparse_table(sparse_table_index, self._learning_rate, prefetch_slots, prefetch_slots_emb) @@ -140,7 +151,8 @@ class DistributedAdam(DistributedOptimizerImplBase): # Todo(guru4elephant): figure out how to support more sparse parameters # currently only support lookup_table worker_skipped_ops = ["lookup_table", "lookup_table_grad"] - ps_param.trainer_param.skip_op.extend(worker_skipped_ops) + if len(ps_param.trainer_param.skip_op) == 0: + ps_param.trainer_param.skip_op.extend(worker_skipped_ops) opt_info = {} opt_info["program_configs"] = program_configs @@ -149,6 +161,7 @@ class DistributedAdam(DistributedOptimizerImplBase): opt_info["optimizer"] = "DownpourSGD" opt_info["fleet_desc"] = ps_param opt_info["worker_skipped_ops"] = worker_skipped_ops + opt_info["use_cvm"] = strategy.get("use_cvm", False) for loss in losses: loss.block.program._fleet_opt = opt_info diff --git a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/ps_pb2.py b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/ps_pb2.py index 5c9b2def0761ac96e81181959852c49f0fd03bd8..378f606d648a244a331f70a52bb3be201b6bff26 100644 --- a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/ps_pb2.py +++ b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/ps_pb2.py @@ -32,7 +32,7 @@ DESCRIPTOR = _descriptor.FileDescriptor( package='paddle', syntax='proto2', serialized_pb=_b( - '\n\x08ps.proto\x12\x06paddle\"\x9e\x02\n\x0bPSParameter\x12\x14\n\x0cworker_class\x18\x01 \x01(\t\x12\x14\n\x0cserver_class\x18\x02 \x01(\t\x12\x16\n\x0einstance_class\x18\x03 \x01(\t\x12-\n\x0cworker_param\x18\x65 \x01(\x0b\x32\x17.paddle.WorkerParameter\x12-\n\x0cserver_param\x18\x66 \x01(\x0b\x32\x17.paddle.ServerParameter\x12\x38\n\rtrainer_param\x18\xad\x02 \x01(\x0b\x32 .paddle.DownpourTrainerParameter\x12\x33\n\x0f\x66s_client_param\x18\xf5\x03 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_sym_db.RegisterFileDescriptor(DESCRIPTOR) @@ -49,8 +49,8 @@ _TABLETYPE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=3489, - serialized_end=3541, ) + serialized_start=3494, + serialized_end=3546, ) _sym_db.RegisterEnumDescriptor(_TABLETYPE) TableType = enum_type_wrapper.EnumTypeWrapper(_TABLETYPE) @@ -134,8 +134,8 @@ _PSCMDID = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=3544, - serialized_end=3861, ) + serialized_start=3549, + serialized_end=3866, ) _sym_db.RegisterEnumDescriptor(_PSCMDID) PsCmdID = enum_type_wrapper.EnumTypeWrapper(_PSCMDID) @@ -168,8 +168,8 @@ _FSCLIENTPARAMETER_FSAPITYPE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=3457, - serialized_end=3487, ) + serialized_start=3462, + serialized_end=3492, ) _sym_db.RegisterEnumDescriptor(_FSCLIENTPARAMETER_FSAPITYPE) _PSPARAMETER = _descriptor.Descriptor( @@ -994,15 +994,15 @@ _TABLEPARAMETER = _descriptor.Descriptor( extension_scope=None, options=None), _descriptor.FieldDescriptor( - name='shared_num', - full_name='paddle.TableParameter.shared_num', + name='shard_num', + full_name='paddle.TableParameter.shard_num', index=2, number=3, type=4, cpp_type=4, label=1, - has_default_value=False, - default_value=0, + has_default_value=True, + default_value=1000, message_type=None, enum_type=None, containing_type=None, @@ -1067,7 +1067,7 @@ _TABLEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=1573, - serialized_end=1764, ) + serialized_end=1769, ) _TABLEACCESSORPARAMETER = _descriptor.Descriptor( name='TableAccessorParameter', @@ -1213,8 +1213,8 @@ _TABLEACCESSORPARAMETER = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=1767, - serialized_end=2136, ) + serialized_start=1772, + serialized_end=2141, ) _DOWNPOURTABLEACCESSORPARAMETER = _descriptor.Descriptor( name='DownpourTableAccessorParameter', @@ -1344,8 +1344,8 @@ _DOWNPOURTABLEACCESSORPARAMETER = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=2139, - serialized_end=2345, ) + serialized_start=2144, + serialized_end=2350, ) _TABLEACCESSORSAVEPARAMETER = _descriptor.Descriptor( name='TableAccessorSaveParameter', @@ -1411,8 +1411,8 @@ _TABLEACCESSORSAVEPARAMETER = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=2347, - serialized_end=2430, ) + serialized_start=2352, + serialized_end=2435, ) _PSREQUESTMESSAGE = _descriptor.Descriptor( name='PsRequestMessage', @@ -1510,8 +1510,8 @@ _PSREQUESTMESSAGE = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=2432, - serialized_end=2533, ) + serialized_start=2437, + serialized_end=2538, ) _SPARSESGDRULEPARAMETER = _descriptor.Descriptor( name='SparseSGDRuleParameter', @@ -1593,8 +1593,8 @@ _SPARSESGDRULEPARAMETER = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=2535, - serialized_end=2654, ) + serialized_start=2540, + serialized_end=2659, ) _DENSESGDRULEPARAMETER = _descriptor.Descriptor( name='DenseSGDRuleParameter', @@ -1692,8 +1692,8 @@ _DENSESGDRULEPARAMETER = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=2657, - serialized_end=2882, ) + serialized_start=2662, + serialized_end=2887, ) _ADAMSGDPARAMETER = _descriptor.Descriptor( name='AdamSGDParameter', @@ -1791,8 +1791,8 @@ _ADAMSGDPARAMETER = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=2885, - serialized_end=3019, ) + serialized_start=2890, + serialized_end=3024, ) _NAIVESGDPARAMETER = _descriptor.Descriptor( name='NaiveSGDParameter', @@ -1842,8 +1842,8 @@ _NAIVESGDPARAMETER = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=3021, - serialized_end=3087, ) + serialized_start=3026, + serialized_end=3092, ) _SUMMARYSGDPARAMETER = _descriptor.Descriptor( name='SummarySGDParameter', @@ -1877,8 +1877,8 @@ _SUMMARYSGDPARAMETER = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=3089, - serialized_end=3148, ) + serialized_start=3094, + serialized_end=3153, ) _MOVINGAVERAGERULEPARAMETER = _descriptor.Descriptor( name='MovingAverageRuleParameter', @@ -1912,8 +1912,8 @@ _MOVINGAVERAGERULEPARAMETER = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=3150, - serialized_end=3196, ) + serialized_start=3155, + serialized_end=3201, ) _PSRESPONSEMESSAGE = _descriptor.Descriptor( name='PsResponseMessage', @@ -1979,8 +1979,8 @@ _PSRESPONSEMESSAGE = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=3198, - serialized_end=3271, ) + serialized_start=3203, + serialized_end=3276, ) _FSCLIENTPARAMETER = _descriptor.Descriptor( name='FsClientParameter', @@ -2110,8 +2110,8 @@ _FSCLIENTPARAMETER = _descriptor.Descriptor( syntax='proto2', extension_ranges=[], oneofs=[], - serialized_start=3274, - serialized_end=3487, ) + serialized_start=3279, + serialized_end=3492, ) _PSPARAMETER.fields_by_name['worker_param'].message_type = _WORKERPARAMETER _PSPARAMETER.fields_by_name['server_param'].message_type = _SERVERPARAMETER diff --git a/python/paddle/fluid/initializer.py b/python/paddle/fluid/initializer.py index 58819efea04218790a0c67c7db2c1e11b9f16f00..0deafaff1bfa51f10eb1cb1d5d2cfdf1dce17da8 100644 --- a/python/paddle/fluid/initializer.py +++ b/python/paddle/fluid/initializer.py @@ -42,8 +42,9 @@ def force_init_on_cpu(): .. code-block:: python - if force_init_on_cpu(): - create_op('force_cpu': force_init_on_cpu()) + if fluid.initializer.force_init_on_cpu(): + step = fluid.layers.create_global_var( + shape=[2,3], value=1.0, dtype='float32') """ return _force_init_on_cpu_ @@ -57,8 +58,9 @@ def init_on_cpu(): Examples: .. code-block:: python - with init_on_cpu(): - step = layers.create_global_var() + with fluid.initializer.init_on_cpu(): + step = fluid.layers.create_global_var( + shape=[2,3], value=1.0, dtype='float32') """ global _force_init_on_cpu_ @@ -131,8 +133,10 @@ class ConstantInitializer(Initializer): Examples: .. code-block:: python - fc = fluid.layers.fc(input=x, size=10, - param_attr=fluid.initializer.Constant(value=2.0)) + x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") + fc = fluid.layers.fc(input=x, size=10, + param_attr=fluid.initializer.Constant(value=2.0)) + """ def __init__(self, value=0.0, force_cpu=False): @@ -208,7 +212,7 @@ class UniformInitializer(Initializer): import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[1], dtype='float32') fc = fluid.layers.fc(input=x, size=10, - param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5)) + param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5)) """ def __init__(self, low=-1.0, high=1.0, seed=0): @@ -288,8 +292,10 @@ class NormalInitializer(Initializer): Examples: .. code-block:: python - fc = fluid.layers.fc(input=x, size=10, - param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0)) + x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") + fc = fluid.layers.fc(input=x, size=10, + param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0)) + """ def __init__(self, loc=0.0, scale=1.0, seed=0): @@ -601,10 +607,11 @@ class MSRAInitializer(Initializer): Examples: .. code-block:: python + + x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") + fc = fluid.layers.fc(input=x, size=10, + param_attr=fluid.initializer.MSRA(uniform=False)) - fc = fluid.layers.fc( - input=queries, size=10, - param_attr=fluid.initializer.MSRA(uniform=False)) """ def __init__(self, uniform=True, fan_in=None, seed=0): @@ -703,19 +710,24 @@ class BilinearInitializer(Initializer): .. code-block:: python - factor = 2 - w_attr = ParamAttr(learning_rate=0., regularizer=L2Decay(0.), - initializer=Bilinear()) - conv_up = fluid.layers.conv2d_transpose( - input, - num_filters=C, - output_size=None, - filter_size=2 * factor - factor % 2, - padding=ceil((factor - 1) / 2.), - stride=factor, - groups=C, - param_attr=w_attr, - bias_attr=False) + factor = 2 + C = 2 + w_attr = fluid.initializer.ParamAttr( + learning_rate=0., + regularizer=fluid.regularizer.L2Decay(0.), + initializer=fluid.initializer.Bilinear()) + x = fluid.layers.data(name="data", shape=[3, 32, 32], + dtype="float32") + conv_up = fluid.layers.conv2d_transpose( + input=x, + num_filters=C, + output_size=None, + filter_size=2 * factor - factor % 2, + padding=int(math.ceil((factor - 1) / 2.)), + stride=factor, + groups=C, + param_attr=w_attr, + bias_attr=False) Where, `num_filters=C` and `groups=C` means this is channel-wise transposed convolution. The filter shape will be (C, 1, K, K) where K is `filer_size`, @@ -824,6 +836,7 @@ class NumpyArrayInitializer(Initializer): Examples: .. code-block:: python + x = fluid.layers.data(name="x", shape=[5], dtype='float32') fc = fluid.layers.fc(input=x, size=10, param_attr=fluid.initializer.NumpyArrayInitializer(numpy.array([1,2]))) """ diff --git a/python/paddle/fluid/install_check.py b/python/paddle/fluid/install_check.py index 3cdd05533f703ac27333daab7ada0c26392a24f5..dd1725b45ac22c7988eb0f137228c7280c742c66 100644 --- a/python/paddle/fluid/install_check.py +++ b/python/paddle/fluid/install_check.py @@ -31,7 +31,7 @@ class SimpleLayer(Layer): super(SimpleLayer, self).__init__(name_scope) self._fc1 = nn.FC(self.full_name(), 3, - ParamAttr(initializer=Constant(value=0.1))) + param_attr=ParamAttr(initializer=Constant(value=0.1))) def forward(self, inputs): x = self._fc1(inputs) diff --git a/python/paddle/fluid/io.py b/python/paddle/fluid/io.py index b573093c3025acead94cc0019f69ec8ca8e1527f..b5dd383a0eb6d80ad3ec5338f6bdcabfef53b51e 100644 --- a/python/paddle/fluid/io.py +++ b/python/paddle/fluid/io.py @@ -25,6 +25,7 @@ from paddle.fluid import layers from paddle.fluid.executor import Executor from paddle.fluid.evaluator import Evaluator from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable, program_guard +from paddle.fluid.log_helper import get_logger from . import reader from .reader import * from . import core @@ -35,9 +36,8 @@ __all__ = [ 'load_persistables', 'save_inference_model', 'load_inference_model' ] + reader.__all__ -logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s') -_logger = logging.getLogger(__name__) -_logger.setLevel(logging.INFO) +_logger = get_logger( + __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') def is_parameter(var): @@ -144,27 +144,37 @@ def save_vars(executor, Examples: .. code-block:: python - exe = fluid.Executor(fluid.CPUPlace()) - param_path = "./my_paddle_model" + import paddle.fluid as fluid + main_prog = fluid.Program() + startup_prog = fluid.Program() + with fluid.program_guard(main_prog, startup_prog): + data = fluid.layers.data(name="img", shape=[64, 784], append_batch_size=False) + w = fluid.layers.create_parameter(shape=[784, 200], dtype='float32', name='fc_w') + b = fluid.layers.create_parameter(shape=[200], dtype='float32', name='fc_b') + hidden_w = fluid.layers.matmul(x=data, y=w) + hidden_b = fluid.layers.elementwise_add(hidden_w, b) + place = fluid.CPUPlace() + exe = fluid.Executor(place) + exe.run(startup_prog) + param_path = "./my_paddle_model" # The first usage: using `main_program` to specify variables def name_has_fc(var): res = "fc" in var.name return res - - prog = fluid.default_main_program() - fluid.io.save_vars(executor=exe, dirname=path, main_program=prog, + fluid.io.save_vars(executor=exe, dirname=param_path, main_program=main_prog, vars=None, predicate = name_has_fc) # All variables in `main_program` whose name includes "fc" will be saved. # And variables are going to be saved separately. # The second usage: using `vars` to specify variables - var_list = [var_a, var_b, var_c] + var_list = [w, b] + path = "./my_paddle_vars" fluid.io.save_vars(executor=exe, dirname=path, vars=var_list, filename="vars_file") # var_a, var_b and var_c will be saved. And they are going to be - # saved in the same file named 'var_file' in the path "./my_paddle_model". + # saved in the same file named 'var_file' in the path "./my_paddle_vars". """ save_dirname = os.path.normpath(dirname) if vars is None: @@ -232,7 +242,9 @@ def save_params(executor, dirname, main_program=None, filename=None): NOTICE: Some variables are not Parameter while they are necessary for training. So you can NOT save and continue your training just by `save_params()` and `load_params()`. Please use `save_persistables()` - and `load_persistables()` instead. + and `load_persistables()` instead. If you want to save your model for + the inference, please use the `save_inference_model` API. You can refer + to :ref:`api_guide_model_save_reader_en` for more details. Args: executor(Executor): The executor to run for saving parameters. @@ -546,27 +558,40 @@ def load_vars(executor, Examples: .. code-block:: python - exe = fluid.Executor(fluid.CPUPlace()) - param_path = "./my_paddle_model" + import paddle.fluid as fluid + main_prog = fluid.Program() + startup_prog = fluid.Program() + with fluid.program_guard(main_prog, startup_prog): + data = fluid.layers.data(name="img", shape=[64, 784], append_batch_size=False) + w = fluid.layers.create_parameter(shape=[784, 200], dtype='float32', name='fc_w') + b = fluid.layers.create_parameter(shape=[200], dtype='float32', name='fc_b') + hidden_w = fluid.layers.matmul(x=data, y=w) + hidden_b = fluid.layers.elementwise_add(hidden_w, b) + place = fluid.CPUPlace() + exe = fluid.Executor(place) + exe.run(startup_prog) + param_path = "./my_paddle_model" # The first usage: using `main_program` to specify variables def name_has_fc(var): res = "fc" in var.name return res - - prog = fluid.default_main_program() - fluid.io.load_vars(executor=exe, dirname=path, main_program=prog, + fluid.io.save_vars(executor=exe, dirname=param_path, main_program=main_prog, + vars=None, predicate=name_has_fc) + fluid.io.load_vars(executor=exe, dirname=param_path, main_program=main_prog, vars=None, predicate=name_has_fc) # All variables in `main_program` whose name includes "fc" will be loaded. # And all the variables are supposed to have been saved in differnet files. - # The second usage: using `vars` to specify variables - var_list = [var_a, var_b, var_c] + path = "./my_paddle_vars" + var_list = [w, b] + fluid.io.save_vars(executor=exe, dirname=path, vars=var_list, + filename="vars_file") fluid.io.load_vars(executor=exe, dirname=path, vars=var_list, filename="vars_file") - # var_a, var_b and var_c will be loaded. And they are supposed to haven - # been saved in the same file named 'var_file' in the path "./my_paddle_model". + # w and b will be loaded. And they are supposed to haven + # been saved in the same file named 'var_file' in the path "./my_paddle_vars". """ load_dirname = os.path.normpath(dirname) if vars is None: @@ -635,6 +660,9 @@ def load_params(executor, dirname, main_program=None, filename=None): training. So you can NOT save and continue your training just by `save_params()` and `load_params()`. Please use `save_persistables()` and `load_persistables()` instead. + If you want to load the pre-trained model structure and parameters + for the inference, please use the `load_inference_model` API. You can + refer to :ref:`api_guide_model_save_reader_en` for more details. Args: executor(Executor): The executor to run for loading parameters. @@ -879,10 +907,15 @@ def save_inference_model(dirname, main_program=None, model_filename=None, params_filename=None, - export_for_deployment=True): + export_for_deployment=True, + program_only=False): """ Prune the given `main_program` to build a new program especially for inference, and then save it and all related parameters to given `dirname` by the `executor`. + If you just want to save parameters of your trained model, please use the + `save_params` API. You can refer to :ref:`api_guide_model_save_reader_en` for + more details. + Args: dirname(str): The directory path to save the inference model. @@ -906,6 +939,7 @@ def save_inference_model(dirname, more information will be stored for flexible optimization and re-training. Currently, only True is supported. + program_only(bool): If True, It will save inference program only, and do not save params of Program. Returns: target_var_name_list(list): The fetch variables' name list @@ -1039,6 +1073,12 @@ def save_inference_model(dirname, with open(model_basename + ".main_program", "wb") as f: f.write(main_program.desc.serialize_to_string()) + if program_only: + warnings.warn( + "save_inference_model specified the param `program_only` to True, It will not save params of Program." + ) + return target_var_name_list + main_program._copy_dist_param_info_from(origin_program) if params_filename is not None: @@ -1054,7 +1094,10 @@ def load_inference_model(dirname, params_filename=None, pserver_endpoints=None): """ - Load inference model from a directory + Load inference model from a directory. By this API, you can get the model + structure(inference program) and model parameters. If you just want to load + parameters of the pre-trained model, please use the `load_params` API. + You can refer to :ref:`api_guide_model_save_reader_en` for more details. Args: dirname(str): The directory path @@ -1088,25 +1131,43 @@ def load_inference_model(dirname, Examples: .. code-block:: python - exe = fluid.Executor(fluid.CPUPlace()) + import paddle.fluid as fluid + import numpy as np + main_prog = fluid.Program() + startup_prog = fluid.Program() + with fluid.program_guard(main_prog, startup_prog): + data = fluid.layers.data(name="img", shape=[64, 784], append_batch_size=False) + w = fluid.layers.create_parameter(shape=[784, 200], dtype='float32') + b = fluid.layers.create_parameter(shape=[200], dtype='float32') + hidden_w = fluid.layers.matmul(x=data, y=w) + hidden_b = fluid.layers.elementwise_add(hidden_w, b) + place = fluid.CPUPlace() + exe = fluid.Executor(place) + exe.run(startup_prog) path = "./infer_model" - endpoints = ["127.0.0.1:2023","127.0.0.1:2024"] - [inference_program, feed_target_names, fetch_targets] = - fluid.io.load_inference_model(dirname=path, executor=exe) + fluid.io.save_inference_model(dirname=path, feeded_var_names=['img'], + target_vars=[hidden_b], executor=exe, main_program=main_prog) + tensor_img = np.array(np.random.random((1, 64, 784)), dtype=np.float32) + [inference_program, feed_target_names, fetch_targets] = ( + fluid.io.load_inference_model(dirname=path, executor=exe)) results = exe.run(inference_program, feed={feed_target_names[0]: tensor_img}, fetch_list=fetch_targets) + # endpoints is your pserver endpoints list, the above is just an example + endpoints = ["127.0.0.1:2023","127.0.0.1:2024"] # if we need lookup table, we will use: - fluid.io.load_inference_model(dirname=path, executor=exe, pserver_endpoints=endpoints) + [dist_inference_program, dist_feed_target_names, dist_fetch_targets] = ( + fluid.io.load_inference_model(dirname=path, + executor=exe, + pserver_endpoints=endpoints)) # In this example, the inference program was saved in the # "./infer_model/__model__" and parameters were saved in - # separate files in ""./infer_model". + # separate files in "./infer_model". # After getting inference program, feed target names and # fetch targets, we can use an Executor to run the inference # program to get the inference result. - """ load_dirname = os.path.normpath(dirname) if not os.path.isdir(load_dirname): diff --git a/python/paddle/fluid/layer_helper_base.py b/python/paddle/fluid/layer_helper_base.py index 9eed00b16185d00f30dfd75f03e31fb45cf9567c..cbfd4f45f907d63e4ea581b67350d2e12b9a9f11 100644 --- a/python/paddle/fluid/layer_helper_base.py +++ b/python/paddle/fluid/layer_helper_base.py @@ -85,19 +85,19 @@ class LayerHelperBase(object): block=self.startup_program.global_block()): if out is None: out = block.create_var( - name=unique_name.generate(".".join( + name=unique_name.generate_with_ignorable_key(".".join( [self.name, 'weight_norm_norm'])), dtype=dtype, persistable=False) abs_out = block.create_var( - name=unique_name.generate(".".join( + name=unique_name.generate_with_ignorable_key(".".join( [self.name, 'weight_norm_abs'])), dtype=dtype, persistable=False) block.append_op( type='abs', inputs={'X': x}, outputs={'Out': abs_out}) pow_out = block.create_var( - name=unique_name.generate(".".join( + name=unique_name.generate_with_ignorable_key(".".join( [self.name, 'weight_norm_pow'])), dtype=dtype, persistable=False) @@ -107,7 +107,7 @@ class LayerHelperBase(object): outputs={'Out': pow_out}, attrs={'factor': float(p)}) sum_out = block.create_var( - name=unique_name.generate(".".join( + name=unique_name.generate_with_ignorable_key(".".join( [self.name, 'weight_norm_sum'])), dtype=dtype, persistable=False) @@ -133,7 +133,7 @@ class LayerHelperBase(object): block=self.startup_program.global_block()): if out is None: out = block.create_var( - name=unique_name.generate(".".join( + name=unique_name.generate_with_ignorable_key(".".join( [self.name, 'weight_norm_reshape'])), dtype=dtype, persistable=False) @@ -150,7 +150,7 @@ class LayerHelperBase(object): block=self.startup_program.global_block()): if out is None: out = block.create_var( - name=unique_name.generate(".".join( + name=unique_name.generate_with_ignorable_key(".".join( [self.name, 'weight_norm_transpose'])), dtype=dtype, persistable=False) @@ -168,7 +168,7 @@ class LayerHelperBase(object): """Computes the norm over all dimensions except dim""" if out is None: out = block.create_var( - name=unique_name.generate(".".join( + name=unique_name.generate_with_ignorable_key(".".join( [self.name, 'weight_norm_norm'])), dtype=dtype, persistable=False) @@ -327,7 +327,8 @@ class LayerHelperBase(object): infer_var_type. """ return self.main_program.current_block().create_var( - name=unique_name.generate(".".join([self.name, 'tmp'])), + name=unique_name.generate_with_ignorable_key(".".join( + [self.name, 'tmp'])), dtype=dtype, type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, diff --git a/python/paddle/fluid/layers/collective.py b/python/paddle/fluid/layers/collective.py index 97c290f5a99da513740a79dae6a769c8214cae66..6beddac7aace007f2c37b154a1b941083144da8b 100644 --- a/python/paddle/fluid/layers/collective.py +++ b/python/paddle/fluid/layers/collective.py @@ -33,7 +33,8 @@ def _allreduce(x, out=None, reduce_type="sum", sync_mode=False): if out is None: out = helper.create_variable( - name=unique_name.generate(".".join([x.name, 'tmp'])), + name=unique_name.generate_with_ignorable_key(".".join( + [x.name, 'tmp'])), shape=x.shape, dtype=x.dtype, type=x.type, @@ -46,3 +47,14 @@ def _allreduce(x, out=None, reduce_type="sum", sync_mode=False): attrs={"reduce_type": red_typ_int, "sync_mode": sync_mode}) return out + + +def _broadcast(x, root, sync_mode=False): + helper = LayerHelper("broadcast", **locals()) + helper.append_op( + type='broadcast', + inputs={'X': [x]}, + outputs={'Out': [x]}, + attrs={"sync_mode": sync_mode, + "root": root}) + return x diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py index 2df63d723e6ce91d3819c5e4301b9d5682158d79..d073c15b0239626516c11dad87cc2d04d72eea90 100644 --- a/python/paddle/fluid/layers/control_flow.py +++ b/python/paddle/fluid/layers/control_flow.py @@ -1,4 +1,4 @@ -# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -165,21 +165,31 @@ def Print(input, print the gradients of input tensor. Returns: - Variable: Output tensor, same data with input tensor. + Variable: Output tensor. + NOTES: + The input and output are two different variables, and in the + following process, you should use the output variable but not the input, + otherwise, the print layer doesn't have backward. Examples: - .. code-block:: python + + import paddle.fluid as fluid + + input = fluid.layers.data(name="input", shape=[4, 32, 32], dtype="float32") + input = fluid.layers.Print(input, message = "The content of input layer:") + # value = some_layer(...) + # Print(value, summarize=10, + # message="The content of some_layer: ") - value = some_layer(...) - Print(value, summarize=10, - message="The content of some_layer: ") ''' - helper = LayerHelper('print', **locals()) + helper = LayerHelper('print' + "_" + input.name, **locals()) + output = helper.create_variable_for_type_inference(input.dtype) helper.append_op( type='print', inputs={'In': input}, + outputs={'Out': output}, attrs={ 'first_n': first_n, 'summarize': summarize, @@ -190,7 +200,7 @@ def Print(input, 'print_tensor_lod': print_tensor_lod, 'print_phase': print_phase.upper() }) - return input + return output class BlockGuard(object): @@ -276,27 +286,29 @@ class StaticRNN(object): the same. And the meaning of each axis of input and output are the same.** Examples: - >>> import paddle.fluid as fluid - >>> import paddle.fluid.layers as layers - >>> - >>> vocab_size, hidden_size=10000, 200 - >>> x = layers.data(name="x", shape=[-1, 1, 1], dtype='int64') - >>> x_emb = layers.embedding( - >>> input=x, - >>> size=[vocab_size, hidden_size], - >>> dtype='float32', - >>> is_sparse=False) - >>> x_emb = layers.transpose(x_emb, perm=[1, 0, 2]) - >>> - >>> rnn = fluid.layers.StaticRNN() - >>> with rnn.step(): - >>> word = rnn.step_input(x_emb) - >>> prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word) - >>> hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu') - >>> rnn.update_memory(prev, hidden) # set prev to hidden - >>> rnn.step_output(hidden) - >>> - >>> result = rnn() + .. code-block:: python + + import paddle.fluid as fluid + import paddle.fluid.layers as layers + + vocab_size, hidden_size=10000, 200 + x = layers.data(name="x", shape=[-1, 1, 1], dtype='int64') + x_emb = layers.embedding( + input=x, + size=[vocab_size, hidden_size], + dtype='float32', + is_sparse=False) + x_emb = layers.transpose(x_emb, perm=[1, 0, 2]) + + rnn = fluid.layers.StaticRNN() + with rnn.step(): + word = rnn.step_input(x_emb) + prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word) + hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu') + rnn.update_memory(prev, hidden) # set prev to hidden + rnn.step_output(hidden) + + result = rnn() The StaticRNN will unfold sequence into time steps. Users need to define how to process each time step during the :code:`with` step. @@ -361,6 +373,27 @@ class StaticRNN(object): Returns: The memory variable. + Examples: + .. code-block:: python + + import paddle.fluid as fluid + import paddle.fluid.layers as layers + + vocab_size, hidden_size=10000, 200 + x = layers.data(name="x", shape=[-1, 1, 1], dtype='int64') + x_emb = layers.embedding( + input=x, + size=[vocab_size, hidden_size], + dtype='float32', + is_sparse=False) + x_emb = layers.transpose(x_emb, perm=[1, 0, 2]) + + rnn = fluid.layers.StaticRNN() + with rnn.step(): + word = rnn.step_input(x_emb) + prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word) + hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu') + rnn.update_memory(prev, hidden) """ self._assert_in_rnn_block_('memory') if init is None: @@ -368,7 +401,7 @@ class StaticRNN(object): raise ValueError( "if init is None, memory at least need shape and batch_ref") parent_block = self._parent_block() - var_name = unique_name.generate("@".join( + var_name = unique_name.generate_with_ignorable_key("@".join( [self.helper.name, "memory_boot"])) boot_var = parent_block.create_var( name=var_name, @@ -391,7 +424,8 @@ class StaticRNN(object): return self.memory(init=boot_var) else: pre_mem = self.helper.create_variable( - name=unique_name.generate("@".join([self.helper.name, "mem"])), + name=unique_name.generate_with_ignorable_key("@".join( + [self.helper.name, "mem"])), dtype=init.dtype, shape=init.shape) self.memories[pre_mem.name] = StaticRNNMemoryLink( @@ -601,18 +635,20 @@ class While(object): Examples: .. code-block:: python - - d0 = layers.data("d0", shape=[10], dtype='float32') - data_array = layers.array_write(x=d0, i=i) - array_len = layers.fill_constant(shape=[1],dtype='int64', value=3) - - cond = layers.less_than(x=i, y=array_len) - while_op = layers.While(cond=cond) + + import paddle.fluid as fluid + + i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0) + d0 = fluid.layers.data("d0", shape=[10], dtype='float32') + data_array = fluid.layers.array_write(x=d0, i=i) + array_len = fluid.layers.fill_constant(shape=[1],dtype='int64', value=3) + + cond = fluid.layers.less_than(x=i, y=array_len) + while_op = fluid.layers.While(cond=cond) with while_op.block(): - d = layers.array_read(array=data_array, i=i) - i = layers.increment(x=i, in_place=True) - layers.array_write(result, i=i, array=d) - layers.less_than(x=i, y=array_len, cond=cond) + d = fluid.layers.array_read(array=data_array, i=i) + i = fluid.layers.increment(x=i, value=1, in_place=True) + fluid.layers.less_than(x=i, y=array_len, cond=cond) """ BEFORE_WHILE_BLOCK = 0 @@ -852,6 +888,7 @@ def increment(x, value=1.0, in_place=True): Examples: .. code-block:: python + import paddle.fluid as fluid data = fluid.layers.data(name='data', shape=[1], dtype='float32', append_batch_size=False) data = fluid.layers.increment(x=data, value=3.0, in_place=True) @@ -892,9 +929,10 @@ def array_write(x, i, array=None): Examples: .. code-block:: python + import paddle.fluid as fluid tmp = fluid.layers.zeros(shape=[10], dtype='int32') i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) - arr = layers.array_write(tmp, i=i) + arr = fluid.layers.array_write(tmp, i=i) """ helper = LayerHelper('array_write', **locals()) if array is None: @@ -941,9 +979,6 @@ def less_than(x, y, force_cpu=None, cond=None): """ ${comment} - >>> import paddle.fluid as fluid - >>> less = fluid.layers.less_than(x=label, y=limit) - Args: x(${x_type}): ${x_comment}. y(${y_type}): ${y_comment}. @@ -952,6 +987,13 @@ def less_than(x, y, force_cpu=None, cond=None): Returns: ${out_comment}. + + Examples: + .. code-block:: python + + label = fluid.layers.data(name='y', shape=[1], dtype='int64') + limit = fluid.layers.fill_constant(shape=[1], dtype='int64', value=5) + cond = fluid.layers.less_than(x=label, y=limit) """ helper = LayerHelper("less_than", **locals()) if cond is None: @@ -1096,6 +1138,9 @@ def equal(x, y, cond=None): Examples: .. code-block:: python + import paddle.fluid as fluid + label = fluid.layers.data(name="label", shape=[3,10,32,32], dtype="float32") + limit = fluid.layers.data(name="limit", shape=[3,10,32,32], dtype="float32") less = fluid.layers.equal(x=label, y=limit) """ helper = LayerHelper("equal", **locals()) @@ -1166,6 +1211,7 @@ def array_read(array, i): Examples: .. code-block:: python + import paddle.fluid as fluid array = fluid.layers.create_array(dtype='float32') i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) item = fluid.layers.array_read(array, i) @@ -1240,6 +1286,7 @@ def array_length(array): Examples: .. code-block:: python + import paddle.fluid as fluid tmp = fluid.layers.zeros(shape=[10], dtype='int32') i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) arr = fluid.layers.array_write(tmp, i=i) @@ -1377,23 +1424,30 @@ class Switch(object): Examples: .. code-block:: python + + import paddle.fluid as fluid - lr = fluid.layers.tensor.create_global_var( + lr = fluid.layers.create_global_var( shape=[1], value=0.0, dtype='float32', persistable=True, name="learning_rate") - one_var = tensor.fill_constant( + zero_var = fluid.layers.fill_constant( + shape=[1], dtype='float32', value=0.0) + one_var = fluid.layers.fill_constant( shape=[1], dtype='float32', value=1.0) - two_var = tensor.fill_constant( - shape=[1], dtype='float32', value=2.0) + two_var = fluid.layers.fill_constant( + shape=[1], dtype='float32', value=2.0) + + global_step = fluid.layers.autoincreased_step_counter( + counter_name='@LR_DECAY_COUNTER@', begin=0, step=1) with fluid.layers.control_flow.Switch() as switch: with switch.case(global_step == zero_var): - fluid.layers.tensor.assign(input=one_var, output=lr) + fluid.layers.assign(input=one_var, output=lr) with switch.default(): - fluid.layers.tensor.assign(input=two_var, output=lr) + fluid.layers.assign(input=two_var, output=lr) """ @@ -1403,8 +1457,6 @@ class Switch(object): self.pre_not_conditions = [] def case(self, condition): - """create a new block for this condition - """ if not self.inside_scope: raise ValueError("case should be called inside with") @@ -1426,9 +1478,6 @@ class Switch(object): return ConditionalBlockGuard(cond_block) def default(self): - """ - create a default case for this switch - """ pre_cond_num = len(self.pre_not_conditions) if pre_cond_num == 0: raise ValueError("there should be at least one condition") @@ -1497,8 +1546,12 @@ class IfElse(object): Examples: .. code-block:: python + import paddle.fluid as fluid + + image = fluid.layers.data(name="X", shape=[2, 5, 5], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') limit = fluid.layers.fill_constant_batch_size_like( - input=label, dtype='int64', shape=[1], value=5.0) + input=label, dtype='int64', shape=[1], value=5.0) cond = fluid.layers.less_than(x=label, y=limit) ie = fluid.layers.IfElse(cond) with ie.true_block(): @@ -1536,11 +1589,13 @@ class IfElse(object): if id(x) not in self.input_table: parent_block = self._parent_block() out_true = parent_block.create_var( - name=unique_name.generate('ifelse_input' + self.helper.name), + name=unique_name.generate_with_ignorable_key('ifelse_input' + + self.helper.name), dtype=x.dtype) out_false = parent_block.create_var( - name=unique_name.generate('ifelse_input' + self.helper.name), + name=unique_name.generate_with_ignorable_key('ifelse_input' + + self.helper.name), dtype=x.dtype) parent_block.append_op( type='split_lod_tensor', @@ -1582,7 +1637,7 @@ class IfElse(object): raise TypeError("Each output should be a variable") # create outside tensor outside_out = parent_block.create_var( - name=unique_name.generate("_".join( + name=unique_name.generate_with_ignorable_key("_".join( [self.helper.name, 'output'])), dtype=each_out.dtype) out_table.append(outside_out) @@ -1622,23 +1677,7 @@ class DynamicRNN(object): sample sequence can be different. This API automatically process them in batch. - The input lod must be set. Please reference `lod_tensor` - - >>> import paddle.fluid as fluid - >>> data = fluid.layers.data(name='sentence', dtype='int64', lod_level=1) - >>> embedding = fluid.layers.embedding(input=data, size=[65535, 32], - >>> is_sparse=True) - >>> - >>> drnn = fluid.layers.DynamicRNN() - >>> with drnn.block(): - >>> word = drnn.step_input(embedding) - >>> prev = drnn.memory(shape=[200]) - >>> hidden = fluid.layers.fc(input=[word, prev], size=200, act='relu') - >>> drnn.update_memory(prev, hidden) # set prev to hidden - >>> drnn.output(hidden) - >>> - >>> # last is the last time step of rnn. It is the encoding result. - >>> last = fluid.layers.sequence_last_step(drnn()) + The input lod must be set. Please reference to `lod_tensor`. The dynamic RNN will unfold sequence into timesteps. Users need to define how to process each time step during the :code:`with` block. @@ -1648,10 +1687,30 @@ class DynamicRNN(object): The dynamic RNN can mark multiple variables as its output. Use `drnn()` to get the output sequence. - + NOTES: Currently it is not supported that setting is_sparse to True of any layers within DynamicRNN. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + sentence = fluid.layers.data(name='sentence', shape=[1], dtype='int64', lod_level=1) + embedding = fluid.layers.embedding(input=sentence, size=[65536, 32], is_sparse=True) + + drnn = fluid.layers.DynamicRNN() + with drnn.block(): + word = drnn.step_input(embedding) + prev = drnn.memory(shape=[200]) + hidden = fluid.layers.fc(input=[word, prev], size=200, act='relu') + drnn.update_memory(prev, hidden) # set prev to hidden + drnn.output(hidden) + + # Get the last time step of rnn. It is the encoding result. + rnn_output = drnn() + last = fluid.layers.sequence_last_step(rnn_output) """ BEFORE_RNN = 0 IN_RNN = 1 @@ -1678,8 +1737,8 @@ class DynamicRNN(object): Mark a sequence as a dynamic RNN input. Args: - x(Variable): The input sequence. - level(int): The level of lod used to split steps. Default: 0. + x (Variable): The input sequence which should have lod information. + level (int): The level of lod used to split steps. Default: 0. Returns: The current timestep in the input sequence. @@ -1730,13 +1789,37 @@ class DynamicRNN(object): def static_input(self, x): """ Mark a variable as a RNN input. The input will not be scattered into - time steps. + time steps. It is optional. Args: - x(Variable): The input variable. + x (Variable): The input variable. Returns: The input variable that can access in RNN. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + sentence = fluid.layers.data(name='sentence', dtype='float32', shape=[32], lod_level=1) + encoder_proj = fluid.layers.data(name='encoder_proj', dtype='float32', shape=[32], lod_level=1) + decoder_boot = fluid.layers.data(name='boot', dtype='float32', shape=[10], lod_level=1) + + drnn = fluid.layers.DynamicRNN() + with drnn.block(): + current_word = drnn.step_input(sentence) + encoder_word = drnn.static_input(encoder_proj) + hidden_mem = drnn.memory(init=decoder_boot, need_reorder=True) + fc_1 = fluid.layers.fc(input=encoder_word, size=30, bias_attr=False) + fc_2 = fluid.layers.fc(input=current_word, size=30, bias_attr=False) + decoder_inputs = fc_1 + fc_2 + h, _, _ = fluid.layers.gru_unit(input=decoder_inputs, hidden=hidden_mem, size=30) + drnn.update_memory(hidden_mem, h) + out = fluid.layers.fc(input=h, size=10, bias_attr=True, act='softmax') + drnn.output(out) + + rnn_output = drnn() """ self._assert_in_rnn_block_("static_input") if not isinstance(x, Variable): @@ -1813,54 +1896,51 @@ class DynamicRNN(object): the input variable. It should be set to true when the initialized memory depends on the input sample. - For example, - - >>> import paddle.fluid as fluid - >>> sentence = fluid.layers.data( - >>> name='sentence', dtype='float32', shape=[32]) - >>> boot_memory = fluid.layers.data( - >>> name='boot', dtype='float32', shape=[10]) - >>> - >>> drnn = fluid.layers.DynamicRNN() - >>> with drnn.block(): - >>> word = drnn.step_input(sentence) - >>> memory = drnn.memory(init=boot_memory, need_reorder=True) - >>> hidden = fluid.layers.fc( - >>> input=[word, memory], size=10, act='tanh') - >>> drnn.update_memory(ex_mem=memory, new_mem=hidden) - >>> drnn.output(hidden) - >>> rnn_output = drnn() + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + sentence = fluid.layers.data(name='sentence', shape=[32], dtype='float32', lod_level=1) + boot_memory = fluid.layers.data(name='boot', shape=[10], dtype='float32', lod_level=1) + + drnn = fluid.layers.DynamicRNN() + with drnn.block(): + word = drnn.step_input(sentence) + memory = drnn.memory(init=boot_memory, need_reorder=True) + hidden = fluid.layers.fc(input=[word, memory], size=10, act='tanh') + drnn.update_memory(ex_mem=memory, new_mem=hidden) + drnn.output(hidden) + + rnn_output = drnn() Otherwise, if :code:`shape`, :code:`value`, :code:`dtype` are set, the :code:`memory` will be initialized by this :code:`value`. - For example, + Examples: + .. code-block:: python - >>> import paddle.fluid as fluid - >>> sentence = fluid.layers.data( - >>> name='sentence', dtype='float32', shape=[32]) - >>> - >>> drnn = fluid.layers.DynamicRNN() - >>> with drnn.block(): - >>> word = drnn.step_input(sentence) - >>> memory = drnn.memory(shape=[10], dtype='float32', value=0) - >>> hidden = fluid.layers.fc( - >>> input=[word, memory], size=10, act='tanh') - >>> drnn.update_memory(ex_mem=memory, new_mem=hidden) - >>> drnn.output(hidden) - >>> rnn_output = drnn() + import paddle.fluid as fluid + sentence = fluid.layers.data(name='sentence', dtype='float32', shape=[32], lod_level=1) + + drnn = fluid.layers.DynamicRNN() + with drnn.block(): + word = drnn.step_input(sentence) + memory = drnn.memory(shape=[10], dtype='float32', value=0) + hidden = fluid.layers.fc(input=[word, memory], size=10, act='tanh') + drnn.update_memory(ex_mem=memory, new_mem=hidden) + drnn.output(hidden) - Args: - init(Variable|None): The initialized variable. + rnn_output = drnn() - shape(list|tuple): The memory shape. NOTE the shape does not contain batch_size. + Args: + init(Variable|None): The initialized variable. + shape(list|tuple): The memory shape. The shape does not contain batch_size. value(float): the initalized value. - need_reorder(bool): True if the initialized memory depends on the input sample. - dtype(str|numpy.dtype): The data type of the initialized memory. Returns: @@ -1975,7 +2055,7 @@ class DynamicRNN(object): parent_block = self._parent_block_() for each in outputs: outside_array = parent_block.create_var( - name=unique_name.generate("_".join( + name=unique_name.generate_with_ignorable_key("_".join( [self.helper.name, "output_array", each.name])), type=core.VarDesc.VarType.LOD_TENSOR_ARRAY, dtype=each.dtype) @@ -2012,8 +2092,31 @@ class DynamicRNN(object): method)) -@autodoc() +@templatedoc() def reorder_lod_tensor_by_rank(x, rank_table): + """ + ${comment} + + Args: + + x(${x_type}): ${x_comment} + rank_table(${rank_table_type}): ${rank_table_type} + + Returns: + out(${out_type}): ${out_comment} + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + data_desc = (['input', [9], 0], ['ref', [5], 1]) + data = fluid.layers.data(name=data_desc[0][0], shape=data_desc[0][1]) + rank_data = fluid.layers.data(name=data_desc[1][0], shape=data_desc[1][1]) + table = fluid.layers.control_flow.lod_rank_table(rank_data) + new_data = fluid.layers.reorder_lod_tensor_by_rank( + x=data, rank_table=table) + + """ helper = LayerHelper('reorder_lod_tensor_by_rank', **locals()) helper.is_instance('x', Variable) helper.is_instance('rank_table', Variable) @@ -2046,9 +2149,12 @@ def is_empty(x, cond=None): Examples: .. code-block:: python + import paddle.fluid as fluid + input = fluid.layers.data(name="input", shape=[4, 32, 32], dtype="float32") res = fluid.layers.is_empty(x=input) # or: - fluid.layers.is_empty(x=input, cond=res) + # fluid.layers.is_empty(x=input, cond=res) + """ helper = LayerHelper("is_empty", **locals()) if cond is None: diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py index dd50fc91248bc7a32d60e5dd347061c2c5cbe5bb..36877269faa0b636a672454b3d682b89a5b94a30 100644 --- a/python/paddle/fluid/layers/detection.py +++ b/python/paddle/fluid/layers/detection.py @@ -38,8 +38,9 @@ __all__ = [ 'target_assign', 'detection_output', 'ssd_loss', - 'detection_map', 'rpn_target_assign', + 'retinanet_target_assign', + 'sigmoid_focal_loss', 'anchor_generator', 'roi_perspective_transform', 'generate_proposal_labels', @@ -52,11 +53,171 @@ __all__ = [ 'yolo_box', 'box_clip', 'multiclass_nms', + 'retinanet_detection_output', 'distribute_fpn_proposals', 'box_decoder_and_assign', + 'collect_fpn_proposals', ] +def retinanet_target_assign(bbox_pred, + cls_logits, + anchor_box, + anchor_var, + gt_boxes, + gt_labels, + is_crowd, + im_info, + num_classes=1, + positive_overlap=0.5, + negative_overlap=0.4): + """ + **Target Assign Layer for Retinanet .** + + This layer can be, for given the Intersection-over-Union (IoU) overlap + between anchors and ground truth boxes, to assign classification and + regression targets to each anchor, these target labels are used for training + retinanet. Every anchor is assigned with a length :attr:`num_classes` + one-hot vector of classification targets, and a 4-vector of box regression + targets. The assignment rules are as followed: + + 1. Anchors are assigned to ground-truth boxes when: (i) it has the highest + IoU overlap with a ground-truth box, or (ii) it has an IoU overlap higher + than positive_overlap(0.5) with any ground-truth box. + + 2. Anchors are assigned to background when its IoU ratio is lower than + negative_overlap (0.4) for all ground-truth boxes. + + When an anchor is assigned with a ground-truth box which is the i-th category, + the i-th entry in its C vector of targets is set to 1 and all other entries + are set to 0. When an anchor is assigned with background, all entries are set + to 0. Anchors that are not assigned do not contribute to the training + objective. The regression targets are the encoded ground-truth boxes + associated with the assigned anchors. + + Args: + bbox_pred(Variable): A 3-D Tensor with shape [N, M, 4] represents the + predicted locations of M bounding bboxes. N is the batch size, + and each bounding box has four coordinate values and the layout + is [xmin, ymin, xmax, ymax]. + cls_logits(Variable): A 3-D Tensor with shape [N, M, C] represents the + predicted confidence predictions. N is the batch size, C is the + number of classes (excluding background), M is number of bounding boxes. + anchor_box(Variable): A 2-D Tensor with shape [M, 4] holds M boxes, + each box is represented as [xmin, ymin, xmax, ymax], + [xmin, ymin] is the left top coordinate of the anchor box, + if the input is image feature map, they are close to the origin + of the coordinate system. [xmax, ymax] is the right bottom + coordinate of the anchor box. + anchor_var(Variable): A 2-D Tensor with shape [M,4] holds expanded + variances of anchors. + gt_boxes(Variable): The ground-truth bounding boxes (bboxes) are a 2D + LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth + bboxes of mini-batch input. + gt_labels(variable): The ground-truth labels are a 2D LoDTensor with + shape [Ng, 1], Ng is the total number of ground-truth labels of + mini-batch input. + is_crowd(Variable): A 1-D LoDTensor which indicates ground-truth is crowd. + im_info(Variable): A 2-D LoDTensor with shape [N, 3]. N is the batch size, + 3 is the height, width and scale. + num_classes(int32): The number of classes. + positive_overlap(float): Minimum overlap required between an anchor + and ground-truth box for the (anchor, gt box) pair to be a positive + example. + negative_overlap(float): Maximum overlap allowed between an anchor + and ground-truth box for the (anchor, gt box) pair to be a negative + examples. + + Returns: + tuple: + A tuple(predicted_scores, predicted_location, target_label, + target_bbox, bbox_inside_weight, fg_num) is returned. The + predicted_scores and predicted_location are the predicted result + of the retinanet.The target_label and target_bbox are the ground + truth, respectively. The predicted_location is a 2D Tensor with + shape [F, 4], and the shape of target_bbox is same as the shape of + the predicted_location, F is the number of the foreground + anchors. The predicted_scores is a 2D Tensor with shape + [F + B, C], and the shape of target_label is [F + B, 1], B is the + number of the background anchors, the F and B is depends on the + input of this operator. Bbox_inside_weight represents whether the + predicted location is fake foreground or not and the shape is [F, 4]. + Fg_num is the foreground number (including fake foreground) which + is needed by focal loss. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + bbox_pred = layers.data(name='bbox_pred', shape=[1, 100, 4], + append_batch_size=False, dtype='float32') + cls_logits = layers.data(name='cls_logits', shape=[1, 100, 10], + append_batch_size=False, dtype='float32') + anchor_box = layers.data(name='anchor_box', shape=[100, 4], + append_batch_size=False, dtype='float32') + anchor_var = layers.data(name='anchor_var', shape=[100, 4], + append_batch_size=False, dtype='float32') + gt_boxes = layers.data(name='gt_boxes', shape=[10, 4], + append_batch_size=False, dtype='float32') + gt_labels = layers.data(name='gt_labels', shape=[10, 1], + append_batch_size=False, dtype='float32') + is_crowd = fluid.layers.data(name='is_crowd', shape=[1], + append_batch_size=False, dtype='float32') + im_info = fluid.layers.data(name='im_infoss', shape=[1, 3], + append_batch_size=False, dtype='float32') + loc_pred, score_pred, loc_target, score_target, bbox_inside_weight, fg_num = + fluid.layers.retinanet_target_assign(bbox_pred, cls_logits, anchor_box, + anchor_var, gt_boxes, gt_labels, is_crowd, im_info, 10) + + """ + + helper = LayerHelper('retinanet_target_assign', **locals()) + # Assign target label to anchors + loc_index = helper.create_variable_for_type_inference(dtype='int32') + score_index = helper.create_variable_for_type_inference(dtype='int32') + target_label = helper.create_variable_for_type_inference(dtype='int32') + target_bbox = helper.create_variable_for_type_inference( + dtype=anchor_box.dtype) + bbox_inside_weight = helper.create_variable_for_type_inference( + dtype=anchor_box.dtype) + fg_num = helper.create_variable_for_type_inference(dtype='int32') + helper.append_op( + type="retinanet_target_assign", + inputs={ + 'Anchor': anchor_box, + 'GtBoxes': gt_boxes, + 'GtLabels': gt_labels, + 'IsCrowd': is_crowd, + 'ImInfo': im_info + }, + outputs={ + 'LocationIndex': loc_index, + 'ScoreIndex': score_index, + 'TargetLabel': target_label, + 'TargetBBox': target_bbox, + 'BBoxInsideWeight': bbox_inside_weight, + 'ForegroundNumber': fg_num + }, + attrs={ + 'positive_overlap': positive_overlap, + 'negative_overlap': negative_overlap + }) + + loc_index.stop_gradient = True + score_index.stop_gradient = True + target_label.stop_gradient = True + target_bbox.stop_gradient = True + bbox_inside_weight.stop_gradient = True + fg_num.stop_gradient = True + + cls_logits = nn.reshape(x=cls_logits, shape=(-1, num_classes)) + bbox_pred = nn.reshape(x=bbox_pred, shape=(-1, 4)) + predicted_cls_logits = nn.gather(cls_logits, score_index) + predicted_bbox_pred = nn.gather(bbox_pred, loc_index) + + return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox, bbox_inside_weight, fg_num + + def rpn_target_assign(bbox_pred, cls_logits, anchor_box, @@ -141,19 +302,24 @@ def rpn_target_assign(bbox_pred, Examples: .. code-block:: python - bbox_pred = layers.data(name='bbox_pred', shape=[100, 4], - append_batch_size=False, dtype='float32') - cls_logits = layers.data(name='cls_logits', shape=[100, 1], - append_batch_size=False, dtype='float32') - anchor_box = layers.data(name='anchor_box', shape=[20, 4], - append_batch_size=False, dtype='float32') - gt_boxes = layers.data(name='gt_boxes', shape=[10, 4], - append_batch_size=False, dtype='float32') - loc_pred, score_pred, loc_target, score_target, bbox_inside_weight = - fluid.layers.rpn_target_assign(bbox_pred=bbox_pred, - cls_logits=cls_logits, - anchor_box=anchor_box, - gt_boxes=gt_boxes) + import paddle.fluid as fluid + bbox_pred = fluid.layers.data(name='bbox_pred', shape=[100, 4], + append_batch_size=False, dtype='float32') + cls_logits = fluid.layers.data(name='cls_logits', shape=[100, 1], + append_batch_size=False, dtype='float32') + anchor_box = fluid.layers.data(name='anchor_box', shape=[20, 4], + append_batch_size=False, dtype='float32') + anchor_var = fluid.layers.data(name='anchor_var', shape=[20, 4], + append_batch_size=False, dtype='float32') + gt_boxes = fluid.layers.data(name='gt_boxes', shape=[10, 4], + append_batch_size=False, dtype='float32') + is_crowd = fluid.layers.data(name='is_crowd', shape=[1], + append_batch_size=False, dtype='float32') + im_info = fluid.layers.data(name='im_infoss', shape=[1, 3], + append_batch_size=False, dtype='float32') + loc_pred, score_pred, loc_target, score_target, bbox_inside_weight= + fluid.layers.rpn_target_assign(bbox_pred, cls_logits, + anchor_box, anchor_var, gt_boxes, is_crowd, im_info) """ @@ -204,6 +370,74 @@ def rpn_target_assign(bbox_pred, return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox, bbox_inside_weight +def sigmoid_focal_loss(x, label, fg_num, gamma=2, alpha=0.25): + """ + **Sigmoid Focal Loss Operator.** + + Focal loss is used to address the foreground-background class imbalance existed + on the training phase of one-stage detectors. This operator computes the sigmoid + value for each element in the input tensor, after which focal loss is measured. + + The focal loss is given as followed: + + .. math:: + loss_j = (-label_j * alpha * {(1 - \\sigma(x_j))}^{gamma} * \\log(\\sigma(x_j)) - + (1 - labels_j) * (1 - alpha) * {(\sigma(x_j)}^{ gamma} * \\log(1 - \\sigma(x_j))) + / fg\_num, j = 1,...,K + + We know that + + .. math:: + \\sigma(x_j) = \\frac{1}{1 + \\exp(-x_j)} + + Args: + x(Variable): A 2-D tensor with shape [N, D], where N is the batch size and D is the number + of classes (excluding background). This input is a tensor of logits computed by the + previous operator. + label(Variable): A 2-D tensor with shape [N, 1], which is the probabilistic labels. + fg_num(Variable): A 1-D tensor with shape [1], which is the number of foreground. + + gamma(float): Hyper-parameter to balance the easy and hard examples. Default value is + set to 2.0. + alpha(float): Hyper-parameter to balance the positive and negative example. Default value + is set to 0.25. + + Returns: + out(Variable): A 2-D tensor with shape [N, D], which is the focal loss. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + input = fluid.layers.data( + name='data', shape=[10,80], append_batch_size=False, dtype='float32') + label = fluid.layers.data( + name='label', shape=[10,1], append_batch_size=False, dtype='int32') + fg_num = fluid.layers.data( + name='fg_num', shape=[1], append_batch_size=False, dtype='int32') + loss = fluid.layers.sigmoid_focal_loss(x=input, + label=label, + fg_num=fg_num, + gamma=2., + alpha=0.25) + """ + + helper = LayerHelper("sigmoid_focal_loss", **locals()) + + out = helper.create_variable_for_type_inference(dtype=x.dtype) + + helper.append_op( + type="sigmoid_focal_loss", + inputs={"X": x, + "Label": label, + "FgNum": fg_num}, + attrs={"gamma": gamma, + 'alpha': alpha}, + outputs={"Out": out}) + return out + + def detection_output(loc, scores, prior_box, @@ -275,13 +509,15 @@ def detection_output(loc, Examples: .. code-block:: python - pb = layers.data(name='prior_box', shape=[10, 4], + import paddle.fluid as fluid + + pb = fluid.layers.data(name='prior_box', shape=[10, 4], append_batch_size=False, dtype='float32') - pbv = layers.data(name='prior_box_var', shape=[10, 4], + pbv = fluid.layers.data(name='prior_box_var', shape=[10, 4], append_batch_size=False, dtype='float32') - loc = layers.data(name='target_box', shape=[2, 21, 4], + loc = fluid.layers.data(name='target_box', shape=[2, 21, 4], append_batch_size=False, dtype='float32') - scores = layers.data(name='scores', shape=[2, 21, 10], + scores = fluid.layers.data(name='scores', shape=[2, 21, 10], append_batch_size=False, dtype='float32') nmsed_outs = fluid.layers.detection_output(scores=scores, loc=loc, @@ -327,6 +563,15 @@ def iou_similarity(x, y, name=None): Returns: out(${out_type}): ${out_comment} + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + x = fluid.layers.data(name='x', shape=[4], dtype='float32') + y = fluid.layers.data(name='y', shape=[4], dtype='float32') + iou = fluid.layers.iou_similarity(x=x, y=y) """ helper = LayerHelper("iou_similarity", **locals()) if name is None: @@ -491,6 +736,14 @@ def polygon_box_transform(input, name=None): Returns: output(${output_type}): ${output_comment} + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + input = fluid.layers.data(name='input', shape=[4, 10, 5, 5], + append_batch_size=False, dtype='float32') + out = fluid.layers.polygon_box_transform(input) """ helper = LayerHelper("polygon_box_transform", **locals()) if name is None: @@ -748,6 +1001,7 @@ def detection_map(detect_res, Examples: .. code-block:: python + from fluid.layers import detection detect_res = fluid.layers.data( name='detect_res', shape=[10, 6], @@ -759,7 +1013,7 @@ def detection_map(detect_res, append_batch_size=False, dtype='float32') - map_out = fluid.layers.detection_map(detect_res, label, 21) + map_out = detection.detection_map(detect_res, label, 21) """ helper = LayerHelper("detection_map", **locals()) @@ -905,7 +1159,7 @@ def target_assign(input, this operator assigns classification/regression targets by performing the following steps: - 1. Assigning all outpts based on `match_indices`: + 1. Assigning all outputs based on `match_indices`: .. code-block:: text @@ -952,11 +1206,22 @@ def target_assign(input, .. code-block:: python - matched_indices, matched_dist = fluid.layers.bipartite_match(iou) - gt = layers.data( - name='gt', shape=[1, 1], dtype='int32', lod_level=1) - trg, trg_weight = layers.target_assign( - gt, matched_indices, mismatch_value=0) + import paddle.fluid as fluid + x = fluid.layers.data( + name='x', + shape=[4, 20, 4], + dtype='float', + lod_level=1, + append_batch_size=False) + matched_id = fluid.layers.data( + name='indices', + shape=[8, 20], + dtype='int32', + append_batch_size=False) + trg, trg_weight = fluid.layers.target_assign( + x, + matched_id, + mismatch_value=0) """ helper = LayerHelper('target_assign', **locals()) out = helper.create_variable_for_type_inference(dtype=input.dtype) @@ -1548,6 +1813,16 @@ def multi_box_head(inputs, Examples: .. code-block:: python + import paddle.fluid as fluid + + images = fluid.layers.data(name='data', shape=[3, 300, 300], dtype='float32') + conv1 = fluid.layers.data(name='conv1', shape=[512, 19, 19], dtype='float32') + conv2 = fluid.layers.data(name='conv2', shape=[1024, 10, 10], dtype='float32') + conv3 = fluid.layers.data(name='conv3', shape=[512, 5, 5], dtype='float32') + conv4 = fluid.layers.data(name='conv4', shape=[256, 3, 3], dtype='float32') + conv5 = fluid.layers.data(name='conv5', shape=[256, 2, 2], dtype='float32') + conv6 = fluid.layers.data(name='conv6', shape=[128, 1, 1], dtype='float32') + mbox_locs, mbox_confs, box, var = fluid.layers.multi_box_head( inputs=[conv1, conv2, conv3, conv4, conv5, conv6], image=images, @@ -1831,6 +2106,7 @@ def roi_perspective_transform(input, .. code-block:: python import paddle.fluid as fluid + x = fluid.layers.data(name='x', shape=[256, 28, 28], dtype='float32') rois = fluid.layers.data(name='rois', shape=[8], lod_level=1, dtype='float32') out = fluid.layers.roi_perspective_transform(x, rois, 7, 7, 1.0) @@ -1869,9 +2145,13 @@ def generate_proposal_labels(rpn_rois, bg_thresh_lo=0.0, bbox_reg_weights=[0.1, 0.1, 0.2, 0.2], class_nums=None, - use_random=True): + use_random=True, + is_cls_agnostic=False, + is_cascade_rcnn=False): """ + ** Generate Proposal Labels of Faster-RCNN ** + This operator can be, for given the GenerateProposalOp output bounding boxes and groundtruth, to sample foreground boxes and background boxes, and compute loss target. @@ -1902,6 +2182,28 @@ def generate_proposal_labels(rpn_rois, bbox_reg_weights(list|tuple): Box regression weights. class_nums(int): Class number. use_random(bool): Use random sampling to choose foreground and background boxes. + is_cls_agnostic(bool): bbox regression use class agnostic simply which only represent fg and bg boxes. + is_cascade_rcnn(bool): it will filter some bbox crossing the image's boundary when setting True. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + rpn_rois = fluid.layers.data(name='rpn_rois', shape=[2, 4], + append_batch_size=False, dtype='float32') + gt_classes = fluid.layers.data(name='gt_classes', shape=[8, 1], + append_batch_size=False, dtype='float32') + is_crowd = fluid.layers.data(name='is_crowd', shape=[8, 1], + append_batch_size=False, dtype='float32') + gt_boxes = fluid.layers.data(name='gt_boxes', shape=[8, 4], + append_batch_size=False, dtype='float32') + im_info = fluid.layers.data(name='im_info', shape=[10, 3], + append_batch_size=False, dtype='float32') + rois, labels_int32, bbox_targets, bbox_inside_weights, + bbox_outside_weights = fluid.layers.generate_proposal_labels( + rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, + class_nums=10) + """ helper = LayerHelper('generate_proposal_labels', **locals()) @@ -1940,7 +2242,9 @@ def generate_proposal_labels(rpn_rois, 'bg_thresh_lo': bg_thresh_lo, 'bbox_reg_weights': bbox_reg_weights, 'class_nums': class_nums, - 'use_random': use_random + 'use_random': use_random, + 'is_cls_agnostic': is_cls_agnostic, + 'is_cascade_rcnn': is_cascade_rcnn }) rois.stop_gradient = True @@ -2032,6 +2336,8 @@ def generate_mask_labels(im_info, gt_classes, is_crowd, gt_segms, rois, Examples: .. code-block:: python + import paddle.fluid as fluid + im_info = fluid.layers.data(name="im_info", shape=[3], dtype="float32") gt_classes = fluid.layers.data(name="gt_classes", shape=[1], @@ -2040,15 +2346,19 @@ def generate_mask_labels(im_info, gt_classes, is_crowd, gt_segms, rois, dtype="float32", lod_level=1) gt_masks = fluid.layers.data(name="gt_masks", shape=[2], dtype="float32", lod_level=3) - # rois, labels_int32 can be the output of + # rois, roi_labels can be the output of # fluid.layers.generate_proposal_labels. + rois = fluid.layers.data(name="rois", shape=[4], + dtype="float32", lod_level=1) + roi_labels = fluid.layers.data(name="roi_labels", shape=[1], + dtype="int32", lod_level=1) mask_rois, mask_index, mask_int32 = fluid.layers.generate_mask_labels( im_info=im_info, gt_classes=gt_classes, is_crowd=is_crowd, gt_segms=gt_masks, rois=rois, - labels_int32=labels_int32, + labels_int32=roi_labels, num_classes=81, resolution=14) """ @@ -2142,6 +2452,24 @@ def generate_proposals(scores, width < min_size. 0.1 by default. eta(float): Apply in adaptive NMS, if adaptive threshold > 0.5, adaptive_threshold = adaptive_threshold * eta in each iteration. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + scores = fluid.layers.data(name='scores', shape=[2, 4, 5, 5], + append_batch_size=False, dtype='float32') + bbox_deltas = fluid.layers.data(name='bbox_deltas', shape=[2, 16, 5, 5], + append_batch_size=False, dtype='float32') + im_info = fluid.layers.data(name='im_info', shape=[2, 3], + append_batch_size=False, dtype='float32') + anchors = fluid.layers.data(name='anchors', shape=[5, 5, 4, 4], + append_batch_size=False, dtype='float32') + variances = fluid.layers.data(name='variances', shape=[5, 5, 10, 4], + append_batch_size=False, dtype='float32') + rois, roi_probs = fluid.layers.generate_proposals(scores, bbox_deltas, + im_info, anchors, variances) + """ helper = LayerHelper('generate_proposals', **locals()) @@ -2221,6 +2549,113 @@ def box_clip(input, im_info, name=None): return output +def retinanet_detection_output(bboxes, + scores, + anchors, + im_info, + score_threshold=0.05, + nms_top_k=1000, + keep_top_k=100, + nms_threshold=0.3, + nms_eta=1.): + """ + **Detection Output Layer for Retinanet.** + + This operation is to get the detection results by performing following + steps: + + 1. Decode top-scoring bounding box predictions per FPN level according + to the anchor boxes. + 2. Merge top predictions from all levels and apply multi-class non + maximum suppression (NMS) on them to get the final detections. + + Args: + bboxes(List): A list of tensors from multiple FPN levels. Each + element is a 3-D Tensor with shape [N, Mi, 4] representing the + predicted locations of Mi bounding boxes. N is the batch size, + Mi is the number of bounding boxes from i-th FPN level and each + bounding box has four coordinate values and the layout is + [xmin, ymin, xmax, ymax]. + scores(List): A list of tensors from multiple FPN levels. Each + element is a 3-D Tensor with shape [N, Mi, C] representing the + predicted confidence predictions. N is the batch size, C is the + class number (excluding background), Mi is the number of bounding + boxes from i-th FPN level. For each bounding box, there are total + C scores. + anchors(List): A 2-D Tensor with shape [Mi, 4] represents the locations + of Mi anchor boxes from all FPN level. Each bounding box has four + coordinate values and the layout is [xmin, ymin, xmax, ymax]. + im_info(Variable): A 2-D LoDTensor with shape [N, 3] represents the + image information. N is the batch size, each image information + includes height, width and scale. + score_threshold(float): Threshold to filter out bounding boxes + with a confidence score. + nms_top_k(int): Maximum number of detections per FPN layer to be + kept according to the confidences before NMS. + keep_top_k(int): Number of total bounding boxes to be kept per image after + NMS step. -1 means keeping all bounding boxes after NMS step. + nms_threshold(float): The threshold to be used in NMS. + nms_eta(float): The parameter for adaptive NMS. + + Returns: + Variable: + The detection output is a LoDTensor with shape [No, 6]. + Each row has six values: [label, confidence, xmin, ymin, xmax, ymax]. + `No` is the total number of detections in this mini-batch. For each + instance, the offsets in first dimension are called LoD, the offset + number is N + 1, N is the batch size. The i-th image has + `LoD[i + 1] - LoD[i]` detected results, if it is 0, the i-th image + has no detected results. If all images have no detected results, + LoD will be set to 0, and the output tensor is empty (None). + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + bboxes = layers.data(name='bboxes', shape=[1, 21, 4], + append_batch_size=False, dtype='float32') + scores = layers.data(name='scores', shape=[1, 21, 10], + append_batch_size=False, dtype='float32') + anchors = layers.data(name='anchors', shape=[21, 4], + append_batch_size=False, dtype='float32') + im_info = layers.data(name="im_info", shape=[1, 3], + append_batch_size=False, dtype='float32') + nmsed_outs = fluid.layers.retinanet_detection_output( + bboxes=[bboxes, bboxes], + scores=[scores, scores], + anchors=[anchors, anchors], + im_info=im_info, + score_threshold=0.05, + nms_top_k=1000, + keep_top_k=100, + nms_threshold=0.3, + nms_eta=1.) + """ + + helper = LayerHelper('retinanet_detection_output', **locals()) + output = helper.create_variable_for_type_inference( + dtype=helper.input_dtype('scores')) + helper.append_op( + type="retinanet_detection_output", + inputs={ + 'BBoxes': bboxes, + 'Scores': scores, + 'Anchors': anchors, + 'ImInfo': im_info + }, + attrs={ + 'score_threshold': score_threshold, + 'nms_top_k': nms_top_k, + 'nms_threshold': nms_threshold, + 'keep_top_k': keep_top_k, + 'nms_eta': 1., + }, + outputs={'Out': output}) + output.stop_gradient = True + return output + + def multiclass_nms(bboxes, scores, score_threshold, @@ -2473,3 +2908,68 @@ def box_decoder_and_assign(prior_box, "OutputAssignBox": output_assign_box }) return decoded_box, output_assign_box + + +def collect_fpn_proposals(multi_rois, + multi_scores, + min_level, + max_level, + post_nms_top_n, + name=None): + """ + Concat multi-level RoIs (Region of Interest) and select N RoIs + with respect to multi_scores. This operation performs the following steps: + + 1. Choose num_level RoIs and scores as input: num_level = max_level - min_level + 2. Concat multi-level RoIs and scores + 3. Sort scores and select post_nms_top_n scores + 4. Gather RoIs by selected indices from scores + 5. Re-sort RoIs by corresponding batch_id + + Args: + multi_ros(list): List of RoIs to collect + multi_scores(list): List of scores + min_level(int): The lowest level of FPN layer to collect + max_level(int): The highest level of FPN layer to collect + post_nms_top_n(int): The number of selected RoIs + name(str|None): A name for this layer(optional) + + Returns: + Variable: Output variable of selected RoIs. + + Examples: + .. code-block:: python + + multi_rois = [] + multi_scores = [] + for i in range(4): + multi_rois.append(fluid.layers.data( + name='roi_'+str(i), shape=[4], dtype='float32', lod_level=1)) + for i in range(4): + multi_scores.append(fluid.layers.data( + name='score_'+str(i), shape=[1], dtype='float32', lod_level=1)) + + fpn_rois = fluid.layers.collect_fpn_proposals( + multi_rois=multi_rois, + multi_scores=multi_scores, + min_level=2, + max_level=5, + post_nms_top_n=2000) + """ + + helper = LayerHelper('collect_fpn_proposals', **locals()) + dtype = helper.input_dtype('multi_rois') + num_lvl = max_level - min_level + 1 + input_rois = multi_rois[:num_lvl] + input_scores = multi_scores[:num_lvl] + output_rois = helper.create_variable_for_type_inference(dtype) + output_rois.stop_gradient = True + helper.append_op( + type='collect_fpn_proposals', + inputs={ + 'MultiLevelRois': input_rois, + 'MultiLevelScores': input_scores + }, + outputs={'FpnRois': output_rois}, + attrs={'post_nms_topN': post_nms_top_n}) + return output_rois diff --git a/python/paddle/fluid/layers/device.py b/python/paddle/fluid/layers/device.py index 43ebd160de3fd3d2a491a3ec1fbe0e4085fbd0b1..78226a5201707a192b6fa38e11bfc243f5815a55 100644 --- a/python/paddle/fluid/layers/device.py +++ b/python/paddle/fluid/layers/device.py @@ -30,7 +30,7 @@ __all__ = [] def get_places(device_count=None, device_type=None): helper = LayerHelper('get_places', **locals()) out_places = helper.create_variable( - name=unique_name.generate(helper.name + ".out")) + name=unique_name.generate_with_ignorable_key(helper.name + ".out")) attrs = dict() if device_count is not None: attrs['device_count'] = int(device_count) diff --git a/python/paddle/fluid/layers/io.py b/python/paddle/fluid/layers/io.py index a2538fa0f9d29aa2521415abf3f8035401b5b2c3..79ad36e4a344671d96be735a4b7b05814a86c839 100644 --- a/python/paddle/fluid/layers/io.py +++ b/python/paddle/fluid/layers/io.py @@ -54,6 +54,11 @@ def data(name, All the input variables of this function are passed in as local variables to the LayerHelper constructor. + Notice that paddle would only use :code:`shape` to infer the shapes of + following variables in the network during compile-time. During run-time, + paddle would not check whether the shape of the feeded data matches the + :code:`shape` settings in this function. + Args: name(str): The name/alias of the function shape(list): Tuple declaring the shape. If :code:`append_batch_size` is @@ -62,9 +67,12 @@ def data(name, should be considered as the shape of the batched data. append_batch_size(bool): 1. If true, it prepends -1 to the shape. - For example if shape=[1], the resulting shape is [-1, 1]. - 2. If shape contains -1, such as shape=[1, -1], - append_batch_size will be enforced to be be False (ineffective). + For example if shape=[1], the resulting shape is [-1, 1]. This will + be useful to set different batch size at run time. + 2. If shape contains -1, such as shape=[1, -1]. + append_batch_size will be enforced to be be False (ineffective) + because PaddlePaddle cannot set more than 1 unknown number on the + shape. dtype(np.dtype|VarType|str): The type of data : float32, float16, int etc type(VarType): The output type. By default it is LOD_TENSOR. lod_level(int): The LoD Level. 0 means the input data is not a sequence. @@ -652,11 +660,11 @@ def py_reader(capacity, This layer returns a Reader Variable. The Reader provides :code:`decorate_paddle_reader()` and :code:`decorate_tensor_provider()` to set a Python generator as the data - source in Python side. When :code:`Executor::Run()` is invoked in C++ - side, the data from the generator would be read automatically. Unlike - :code:`DataFeeder.feed()`, the data reading process and - :code:`Executor::Run()` process can run in parallel using - :code:`py_reader`. The :code:`start()` method of the Reader should be + source. More details :ref:`user_guide_use_py_reader_en` . When + :code:`Executor::Run()` is invoked in C++ side, the data from the generator + would be read automatically. Unlike :code:`DataFeeder.feed()`, the data + reading process and :code:`Executor::Run()` process can run in parallel + using :code:`py_reader`. The :code:`start()` method of the Reader should be called when each pass begins, while the :code:`reset()` method should be called when the pass ends and :code:`fluid.core.EOFException` raises. Note that :code:`Program.clone()` method cannot clone :code:`py_reader`. @@ -894,6 +902,7 @@ def open_files(filenames, Examples: .. code-block:: python + import paddle.fluid as fluid reader = fluid.layers.io.open_files(filenames=['./data1.recordio', './data2.recordio'], shapes=[(3,224,224), (1,)], @@ -991,6 +1000,19 @@ def shuffle(reader, buffer_size): Returns: callable: the new reader whose output is shuffled. + + Examples: + .. code-block:: python + + raw_reader = fluid.layers.io.open_files(filenames=['./data1.recordio', + './data2.recordio'], + shapes=[(3,224,224), (1,)], + lod_levels=[0, 0], + dtypes=['float32', 'int64'], + thread_num=2, + buffer_size=2) + batch_reader = fluid.layers.batch(reader=raw_reader, batch_size=5) + shuffle_reader = fluid.layers.shuffle(reader=batch_reader, buffer_size=5000) """ return __create_unshared_decorated_reader__( 'create_shuffle_reader', reader, {'buffer_size': int(buffer_size)}) @@ -1054,7 +1076,8 @@ def double_buffer(reader, place=None, name=None): Examples: - >>> reader = fluid.layers.open_files(filenames=['somefile'], + >>> import paddle.fluid as fluid + >>> reader = fluid.layers.open_files(filenames=['mnist.recordio'], >>> shapes=[[-1, 784], [-1, 1]], >>> dtypes=['float32', 'int64']) >>> reader = fluid.layers.double_buffer(reader) @@ -1089,15 +1112,16 @@ def read_file(reader): Examples: .. code-block:: python - + + import paddle.fluid as fluid data_file = fluid.layers.open_files( filenames=['mnist.recordio'], shapes=[(-1, 748), (-1, 1)], lod_levels=[0, 0], dtypes=["float32", "int64"]) - data_file = fluid.layers.double_buffer( + data_file = fluid.layers.double_buffer( fluid.layers.batch(data_file, batch_size=64)) - input, label = fluid.layers.read_file(data_file) + input, label = fluid.layers.read_file(data_file) """ helper = LayerHelper('read_file') out = [ diff --git a/python/paddle/fluid/layers/learning_rate_scheduler.py b/python/paddle/fluid/layers/learning_rate_scheduler.py index a9fdb10ae017b2b639153e1819b1275b6589624c..278830c8e270b5874553db4e06f5fdb6f4a33188 100644 --- a/python/paddle/fluid/layers/learning_rate_scheduler.py +++ b/python/paddle/fluid/layers/learning_rate_scheduler.py @@ -124,14 +124,14 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False): Examples: .. code-block:: python + import paddle.fluid as fluid base_lr = 0.1 sgd_optimizer = fluid.optimizer.SGD( - learning_rate=fluid.layers.exponential_decay( - learning_rate=base_lr, - decay_steps=10000, - decay_rate=0.5, - staircase=True)) - sgd_optimizer.minimize(avg_cost) + learning_rate=fluid.layers.exponential_decay( + learning_rate=base_lr, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) """ with default_main_program()._lr_schedule_guard(): @@ -167,6 +167,19 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False): Returns: The decayed learning rate + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + base_lr = 0.1 + sgd_optimizer = fluid.optimizer.SGD( + learning_rate=fluid.layers.natural_exp_decay( + learning_rate=base_lr, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) + """ with default_main_program()._lr_schedule_guard(): if imperative_base.enabled(): @@ -210,14 +223,14 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False): Examples: .. code-block:: python + import paddle.fluid as fluid base_lr = 0.1 sgd_optimizer = fluid.optimizer.SGD( - learning_rate=fluid.layers.inverse_time_decay( - learning_rate=base_lr, - decay_steps=10000, - decay_rate=0.5, - staircase=True)) - sgd_optimizer.minimize(avg_cost) + learning_rate=fluid.layers.natural_exp_decay( + learning_rate=base_lr, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) """ with default_main_program()._lr_schedule_guard(): if imperative_base.enabled(): diff --git a/python/paddle/fluid/layers/math_op_patch.py b/python/paddle/fluid/layers/math_op_patch.py index 734383655cf6a85015750ab432c0f6697dd6a9b8..90689c0f3775cdf9697eb3453ad292102a935207 100644 --- a/python/paddle/fluid/layers/math_op_patch.py +++ b/python/paddle/fluid/layers/math_op_patch.py @@ -42,7 +42,9 @@ def monkey_patch_variable(): 'shape': shape, 'value': value, 'force_cpu': force_init_on_cpu() - }) + }, + stop_gradient=True) + var.stop_gradient = True return var def create_scalar(block, value, dtype): @@ -68,7 +70,10 @@ def monkey_patch_variable(): 'value': value, 'input_dim_idx': batch_dim, 'output_dim_idx': batch_dim - }) + }, + stop_gradient=True) + + var.stop_gradient = True return var def astype(self, dtype): diff --git a/python/paddle/fluid/layers/metric_op.py b/python/paddle/fluid/layers/metric_op.py index b2d2c93ead80d781d0a55ca541a1b0bb4232ad81..8fd50d28c39126eb7ea2c192457968ec01763774 100644 --- a/python/paddle/fluid/layers/metric_op.py +++ b/python/paddle/fluid/layers/metric_op.py @@ -50,10 +50,11 @@ def accuracy(input, label, k=1, correct=None, total=None): Examples: .. code-block:: python + import paddle.fluid as fluid data = fluid.layers.data(name="data", shape=[-1, 32, 32], dtype="float32") - label = fluid.layers.data(name="data", shape=[-1,1], dtype="int32") + label = fluid.layers.data(name="label", shape=[-1,1], dtype="int32") predict = fluid.layers.fc(input=data, size=10) - acc = fluid.layers.accuracy(input=predict, label=label, k=5) + accuracy_out = fluid.layers.accuracy(input=predict, label=label, k=5) """ helper = LayerHelper("accuracy", **locals()) @@ -119,9 +120,11 @@ def auc(input, Examples: .. code-block:: python - # network is a binary classification model and label the ground truth - prediction = network(image, is_infer=True) - auc_out=fluid.layers.auc(input=prediction, label=label) + import paddle.fluid as fluid + data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") + label = fluid.layers.data(name="label", shape=[1], dtype="int32") + predict = fluid.layers.fc(input=data, size=2) + auc_out = fluid.layers.auc(input=predict, label=label) """ helper = LayerHelper("auc", **locals()) auc_out = helper.create_variable_for_type_inference(dtype="float64") diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index d179f56c6ca3fb482561fcda2b27316670c99696..1c5fa4aa3ff42be01dcc254f22ab8fbdb693d294 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -28,7 +28,7 @@ from ..framework import Variable, OpProtoHolder, in_dygraph_mode from ..dygraph import base from ..param_attr import ParamAttr from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_ -from .tensor import concat, assign +from .tensor import concat, assign, fill_constant from . import utils from .. import unique_name from functools import reduce @@ -201,6 +201,10 @@ __all__ = [ 'fsp_matrix', 'continuous_value_model', 'where', + 'sign', + 'deformable_conv', + 'unfold', + 'deformable_roi_pooling', ] kIgnoreIndex = -100 @@ -384,9 +388,9 @@ def embedding(input, Examples: .. code-block:: python - dict_size = len(dataset.ids) - data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32') - fc = fluid.layers.embedding(input=data, size=[dict_size, 16]) + import paddle.fluid as fluid + data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) + emb = fluid.layers.embedding(input=data, size=[128, 64]) """ helper = LayerHelper('embedding', **locals()) @@ -482,10 +486,18 @@ def dynamic_lstm(input, Examples: .. code-block:: python - + + emb_dim = 256 + vocab_size = 10000 hidden_dim = 512 - forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4, + + data = fluid.layers.data(name='x', shape=[1], + dtype='int32', lod_level=1) + emb = fluid.layers.embedding(input=data, size=[vocab_size, emb_dim], is_sparse=True) + + forward_proj = fluid.layers.fc(input=emb, size=hidden_dim * 4, bias_attr=False) + forward, _ = fluid.layers.dynamic_lstm( input=forward_proj, size=hidden_dim * 4, use_peepholes=False) """ @@ -624,20 +636,23 @@ def lstm(input, Examples: .. code-block:: python - - input = embedding + + emb_dim = 256 + vocab_size = 10000 + data = fluid.layers.data(name='x', shape=[-1, 100, 1], + dtype='int32') + emb = fluid.layers.embedding(input=data, size=[vocab_size, emb_dim], is_sparse=True) batch_size = 20 max_len = 100 dropout_prob = 0.2 input_size = 100 hidden_size = 150 num_layers = 1 - init_hidden1 = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0, stop_grad=False) - init_cell1 = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0, stop_grad=False) - - rnn_out, last_h, last_c = layers.lstm( input, init_h, init_c, \ - max_len, dropout_prob, input_size, hidden_size, \ - num_layers) + init_h = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 ) + init_c = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 ) + rnn_out, last_h, last_c = layers.lstm( emb, init_h, init_c, \ + max_len, hidden_size, num_layers, \ + dropout_prob=dropout_prob) """ helper = LayerHelper('cudnn_lstm', **locals()) @@ -1040,6 +1055,8 @@ def dynamic_gru(input, .. code-block:: python + import paddle.fluid as fluid + dict_dim, emb_dim = 128, 64 data = fluid.layers.data(name='sequence', shape=[1], dtype='int32', lod_level=1) @@ -1177,10 +1194,17 @@ def gru_unit(input, .. code-block:: python - # assuming we have x_t_data and prev_hidden of size=10 - x_t = fluid.layers.fc(input=x_t_data, size=30) - hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t, - hidden = prev_hidden) + import paddle.fluid as fluid + + dict_dim, emb_dim = 128, 64 + data = fluid.layers.data(name='step_data', shape=[1], dtype='int32') + emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim]) + hidden_dim = 512 + x = fluid.layers.fc(input=emb, size=hidden_dim * 3) + pre_hidden = fluid.layers.data( + name='pre_hidden', shape=[hidden_dim], dtype='float32') + hidden = fluid.layers.gru_unit( + input=x, hidden=pre_hidden, size=hidden_dim * 3) """ activation_dict = dict( @@ -1244,6 +1268,19 @@ def linear_chain_crf(input, label, param_attr=None): output(${transition_exps_type}): ${transition_exps_comment} \n output(${log_likelihood_type}): ${log_likelihood_comment} + Examples: + .. code-block:: python + + import paddle.fluid as fluid + emission = fluid.layers.data(name='emission', shape=[1000], dtype='float32') + target = fluid.layers.data(name='target', shape=[1], dtype='int32') + crf_cost = fluid.layers.linear_chain_crf( + input=emission, + label=target, + param_attr=fluid.ParamAttr( + name='crfw', + learning_rate=0.2)) + """ helper = LayerHelper('linear_chain_crf', **locals()) size = input.shape[1] @@ -1530,14 +1567,16 @@ def cross_entropy2(input, label, ignore_index=kIgnoreIndex): def bpr_loss(input, label, name=None): """ - Bayesian Personalized Ranking Loss Operator. + **Bayesian Personalized Ranking Loss Operator** This operator belongs to pairwise ranking loss. Label is the desired item. The loss at a given point in one session is defined as: - $Y[i] = -\frac{1}{N_{i}-1} * \sum_{0\le j(https://arxiv.org/abs/1511.06939) + neural networks>. Args: input (Variable|list): a 2-D tensor with shape [N x D], where N is the @@ -1553,9 +1592,15 @@ def bpr_loss(input, label, name=None): Examples: .. code-block:: python + import paddle.fluid as fluid + + neg_size = 10 + label = fluid.layers.data( + name="label", shape=[1], dtype="int64") + predict = fluid.layers.data( + name="predict", shape=[neg_size + 1], dtype="float32") cost = fluid.layers.bpr_loss(input=predict, label=label) """ - helper = LayerHelper('bpr_loss', **locals()) out = helper.create_variable_for_type_inference(dtype=input.dtype) helper.append_op( @@ -1702,10 +1747,21 @@ def chunk_eval(input, Examples: .. code-block:: python + import paddle.fluid as fluid + + dict_size = 10000 + label_dict_len = 7 + sequence = fluid.layers.data( + name='id', shape=[1], lod_level=1, dtype='int64') + embedding = fluid.layers.embedding( + input=sequence, size=[dict_size, 512]) + hidden = fluid.layers.fc(input=embedding, size=512) + label = fluid.layers.data( + name='label', shape=[1], lod_level=1, dtype='int32') crf = fluid.layers.linear_chain_crf( - input=hidden, label=label, param_attr=ParamAttr(name="crfw")) + input=hidden, label=label, param_attr=fluid.ParamAttr(name="crfw")) crf_decode = fluid.layers.crf_decoding( - input=hidden, param_attr=ParamAttr(name="crfw")) + input=hidden, param_attr=fluid.ParamAttr(name="crfw")) fluid.layers.chunk_eval( input=crf_decode, label=label, @@ -1781,6 +1837,13 @@ def sequence_conv(input, Returns: Variable: output of sequence_conv + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + x = fluid.layers.data(name='x', shape=[10,10], append_batch_size=False, dtype='float32') + x_conved = fluid.layers.sequence_conv(x,2) """ assert not in_dygraph_mode(), ( @@ -1901,6 +1964,8 @@ def softmax(input, use_cudnn=False, name=None, axis=-1): .. code-block:: python + import paddle.fluid as fluid + x = fluid.layers.data(name='x', shape=[2], dtype='float32') fc = fluid.layers.fc(input=x, size=10) # perform softmax in the second dimension softmax = fluid.layers.softmax(input=fc, axis=1) @@ -2172,7 +2237,7 @@ def conv3d(input, Args: input (Variable): The input image with [N, C, D, H, W] format. - num_filters(int): The number of filter. It is as same as the output + num_filters(int): The number of filter. It is as same as the output image channel. filter_size (int|tuple|None): The filter size. If filter_size is a tuple, it must contain three integers, (filter_size_D, filter_size_H, filter_size_W). @@ -2283,7 +2348,7 @@ def conv3d(input, return helper.append_activation(pre_act) -def sequence_pool(input, pool_type, is_test=False): +def sequence_pool(input, pool_type, is_test=False, pad_value=0.0): """ This function add the operator for sequence pooling. It pools features of all time-steps of each instance, and is applied @@ -2298,29 +2363,32 @@ def sequence_pool(input, pool_type, is_test=False): .. code-block:: text - x is a 1-level LoDTensor: - x.lod = [[2, 3, 2]] + x is a 1-level LoDTensor and **pad_value** = 0.0: + x.lod = [[2, 3, 2, 0]] x.data = [1, 3, 2, 4, 6, 5, 1] x.dims = [7, 1] then output is a Tensor: - out.dim = [3, 1] + out.dim = [4, 1] with condition len(x.lod[-1]) == out.dims[0] for different pool_type: - average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2 - sum : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1 - sqrt : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2), + average: out.data = [2, 4, 3, 0.0], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2 + sum : out.data = [4, 12, 6, 0.0], where 4=1+3, 12=2+4+6, 6=5+1 + sqrt : out.data = [2.82, 6.93, 4.24, 0.0], where 2.82=(1+3)/sqrt(2), 6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2) - max : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1) - last : out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1) - first : out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1) + max : out.data = [3, 6, 5, 0.0], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1) + last : out.data = [3, 6, 1, 0.0], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1) + first : out.data = [1, 2, 5, 0.0], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1) + + and all above 0.0 = **pad_value**. Args: - input(variable): The input variable which is a LoDTensor. + input (variable): The input variable which is a LoDTensor. pool_type (string): The pooling type of sequence_pool. It supports average, sum, sqrt and max. - is_test(bool, Default False): Used distinguish training from scoring mode. + is_test (bool): Used to distinguish training from scoring mode. Default False. + pad_value (float): Used to pad the pooling result for empty input sequence. Returns: The sequence pooling variable which is a Tensor. @@ -2329,6 +2397,8 @@ def sequence_pool(input, pool_type, is_test=False): .. code-block:: python + import paddle.fluid as fluid + x = fluid.layers.data(name='x', shape=[7, 1], dtype='float32', lod_level=1) avg_x = fluid.layers.sequence_pool(input=x, pool_type='average') @@ -2350,8 +2420,11 @@ def sequence_pool(input, pool_type, is_test=False): inputs={"X": input}, outputs={"Out": pool_out, "MaxIndex": max_index}, - attrs={"pooltype": pool_type.upper(), - "is_test": is_test}) + attrs={ + "pooltype": pool_type.upper(), + "is_test": is_test, + "pad_value": pad_value + }) # when pool_type is max, variable max_index is initialized, # so we stop the gradient explicitly here @@ -2377,7 +2450,10 @@ def sequence_concat(input, name=None): Examples: .. code-block:: python - out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3]) + import paddle.fluid as fluid + x = fluid.layers.data(name='x', shape=[10], dtype='float32') + y = fluid.layers.data(name='y', shape=[10], dtype='float32') + out = fluid.layers.sequence_concat(input=[x, y]) """ assert not in_dygraph_mode(), ( "sequence layer is not supported in dygraph mode yet.") @@ -2906,9 +2982,12 @@ def adaptive_pool3d(input, # output[:, :, i, j, k] = # avg(input[:, :, dstart:dend, hstart: hend, wstart: wend]) # + + import paddle.fluid as fluid + data = fluid.layers.data( - name='data', shape=[3, 32, 32], dtype='float32') - pool_out, mask = fluid.layers.adaptive_pool3d( + name='data', shape=[3, 32, 32, 32], dtype='float32') + pool_out = fluid.layers.adaptive_pool3d( input=data, pool_size=[3, 3, 3], pool_type='avg') @@ -3019,18 +3098,24 @@ def batch_norm(input, numerical stability. Default is 1e-5. param_attr(ParamAttr|None): The parameter attribute for Parameter `scale` of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm - will create ParamAttr as param_attr. If the Initializer of the param_attr - is not set, the parameter is initialized with Xavier. Default: None. + will create ParamAttr as param_attr, the name of scale can be set in ParamAttr. + If the Initializer of the param_attr is not set, the parameter is initialized + with Xavier. Default: None. bias_attr(ParamAttr|None): The parameter attribute for the bias of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm - will create ParamAttr as bias_attr. If the Initializer of the bias_attr - is not set, the bias is initialized zero. Default: None. + will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. + If the Initializer of the bias_attr is not set, the bias is initialized zero. + Default: None. data_layout(string, default NCHW): NCHW|NHWC in_place(bool, Default False): Make the input and output of batch norm reuse memory. name(string, Default None): A name for this layer(optional). If set None, the layer will be named automatically. - moving_mean_name(string, Default None): The name of moving_mean which store the global Mean. + moving_mean_name(string, Default None): The name of moving_mean which store the global Mean. If it + is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm + will save global mean with the string. moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance. + If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm + will save global variance with the string. do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not. fuse_with_relu (bool): if True, this OP performs relu after batch norm. use_global_stats(bool, Default False): Whether to use global mean and @@ -3191,9 +3276,11 @@ def data_norm(input, Examples: .. code-block:: python + + import paddle.fluid as fluid - data = fluid.layers.data(input=x, size=200, param_attr='fc1.w') - hidden2 = fluid.layers.data_norm(input=hidden1) + hidden1 = fluid.layers.data(name="hidden1", shape=[200]) + hidden2 = fluid.layers.data_norm(name="hidden2", input=hidden1) """ helper = LayerHelper('data_norm', **locals()) dtype = helper.input_dtype() @@ -3499,10 +3586,13 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None): Variable: A tensor variable of weight parameters after spectral normalization. Examples: + .. code-block:: python - >>> weight = fluid.layers.data(name='weight', shape=[8, 32, 32], - >>> dtype='float32') - >>> x = fluid.layers.spectral_norm(weight=data, dim=1, power_iters=2) + import paddle.fluid as fluid + + weight = fluid.layers.data(name='weight', shape=[2, 8, 32, 32], + append_batch_size=False, dtype='float32') + x = fluid.layers.spectral_norm(weight=weight, dim=1, power_iters=2) """ helper = LayerHelper('spectral_norm', **locals()) dtype = weight.dtype @@ -3964,7 +4054,8 @@ def sequence_expand(x, y, ref_level=-1, name=None): Examples: .. code-block:: python - + + import paddle.fluid.layers as layers x = fluid.layers.data(name='x', shape=[10], dtype='float32') y = fluid.layers.data(name='y', shape=[10, 20], dtype='float32', lod_level=1) @@ -4032,6 +4123,7 @@ def sequence_expand_as(x, y, name=None): Examples: .. code-block:: python + import paddle.fluid.layers as layers x = fluid.layers.data(name='x', shape=[10], dtype='float32') y = fluid.layers.data(name='y', shape=[10, 20], @@ -4249,16 +4341,25 @@ def beam_search(pre_ids, Examples: .. code-block:: python + import paddle.fluid as fluid + # Suppose `probs` contains predicted results from the computation # cell and `pre_ids` and `pre_scores` is the output of beam_search # at previous step. - topk_scores, topk_indices = layers.topk(probs, k=beam_size) - accu_scores = layers.elementwise_add( - x=layers.log(x=topk_scores)), - y=layers.reshape( - pre_scores, shape=[-1]), + beam_size = 4 + end_id = 1 + pre_ids = fluid.layers.data( + name='pre_id', shape=[1], lod_level=2, dtype='int64') + pre_scores = fluid.layers.data( + name='pre_scores', shape=[1], lod_level=2, dtype='float32') + probs = fluid.layers.data( + name='probs', shape=[10000], dtype='float32') + topk_scores, topk_indices = fluid.layers.topk(probs, k=beam_size) + accu_scores = fluid.layers.elementwise_add( + x=fluid.layers.log(x=topk_scores), + y=fluid.layers.reshape(pre_scores, shape=[-1]), axis=0) - selected_ids, selected_scores = layers.beam_search( + selected_ids, selected_scores = fluid.layers.beam_search( pre_ids=pre_ids, pre_scores=pre_scores, ids=topk_indices, @@ -4332,9 +4433,13 @@ def beam_search_decode(ids, scores, beam_size, end_id, name=None): Examples: .. code-block:: python + import paddle.fluid as fluid + # Suppose `ids` and `scores` are LodTensorArray variables reserving # the selected ids and scores of all steps - finished_ids, finished_scores = layers.beam_search_decode( + ids = fluid.layers.create_array(dtype='int64') + scores = fluid.layers.create_array(dtype='float32') + finished_ids, finished_scores = fluid.layers.beam_search_decode( ids, scores, beam_size=5, end_id=0) """ helper = LayerHelper('beam_search_decode', **locals()) @@ -4394,7 +4499,7 @@ def lstm_unit(x_t, i_t = \sigma(L_{i_t}) - This layer has two outputs including :math:`h_t` and :math:`o_t`. + This layer has two outputs including :math:`h_t` and :math:`c_t`. Args: x_t (Variable): The input value of current step, a 2-D tensor with shape @@ -4432,12 +4537,19 @@ def lstm_unit(x_t, .. code-block:: python - x_t = fluid.layers.fc(input=x_t_data, size=10) - prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30) - prev_cell = fluid.layers.fc(input=prev_cell_data, size=30) - hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t, - hidden_t_prev=prev_hidden, - cell_t_prev=prev_cell) + import paddle.fluid as fluid + + dict_dim, emb_dim, hidden_dim = 128, 64, 512 + data = fluid.layers.data(name='step_data', shape=[1], dtype='int32') + x = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim]) + pre_hidden = fluid.layers.data( + name='pre_hidden', shape=[hidden_dim], dtype='float32') + pre_cell = fluid.layers.data( + name='pre_cell', shape=[hidden_dim], dtype='float32') + hidden = fluid.layers.lstm_unit( + x_t=x, + hidden_t_prev=pre_hidden, + cell_t_prev=pre_cell) """ helper = LayerHelper('lstm_unit', **locals()) @@ -4506,21 +4618,24 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None): Examples: .. code-block:: python + import paddle.fluid as fluid # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the corresponding output tensor. + x = fluid.layers.data(name='x', shape=[4, 2], dtype='float32') fluid.layers.reduce_sum(x) # [3.5] fluid.layers.reduce_sum(x, dim=0) # [0.3, 0.5, 1.1, 1.6] fluid.layers.reduce_sum(x, dim=-1) # [1.9, 1.6] fluid.layers.reduce_sum(x, dim=1, keep_dim=True) # [[1.9], [1.6]] - # x is a Tensor variable with shape [2, 2, 2] and elements as below: + # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1, 2], [3, 4]], # [[5, 6], [7, 8]]] # Each example is followed by the corresponding output tensor. - fluid.layers.reduce_sum(x, dim=[1, 2]) # [10, 26] - fluid.layers.reduce_sum(x, dim=[0, 1]) # [16, 20] + y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32') + fluid.layers.reduce_sum(y, dim=[1, 2]) # [10, 26] + fluid.layers.reduce_sum(y, dim=[0, 1]) # [16, 20] """ helper = LayerHelper('reduce_sum', **locals()) @@ -4563,22 +4678,24 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None): Examples: .. code-block:: python + import paddle.fluid as fluid # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the correspending output tensor. + x = fluid.layers.data(name='x', shape=[4, 2], dtype='float32') fluid.layers.reduce_mean(x) # [0.4375] fluid.layers.reduce_mean(x, dim=0) # [0.15, 0.25, 0.55, 0.8] fluid.layers.reduce_mean(x, dim=-1) # [0.475, 0.4] - fluid.layers.reduce_mean( - x, dim=1, keep_dim=True) # [[0.475], [0.4]] + fluid.layers.reduce_mean(x, dim=1, keep_dim=True) # [[0.475], [0.4]] - # x is a Tensor variable with shape [2, 2, 2] and elements as below: + # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1.0, 2.0], [3.0, 4.0]], # [[5.0, 6.0], [7.0, 8.0]]] # Each example is followed by the correspending output tensor. - fluid.layers.reduce_mean(x, dim=[1, 2]) # [2.5, 6.5] - fluid.layers.reduce_mean(x, dim=[0, 1]) # [4.0, 5.0] + y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32') + fluid.layers.reduce_mean(y, dim=[1, 2]) # [2.5, 6.5] + fluid.layers.reduce_mean(y, dim=[0, 1]) # [4.0, 5.0] """ helper = LayerHelper('reduce_mean', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) @@ -4619,21 +4736,24 @@ def reduce_max(input, dim=None, keep_dim=False, name=None): Examples: .. code-block:: python + import paddle.fluid as fluid # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the correspending output tensor. + x = fluid.layers.data(name='x', shape=[4, 2], dtype='float32') fluid.layers.reduce_max(x) # [0.9] fluid.layers.reduce_max(x, dim=0) # [0.2, 0.3, 0.6, 0.9] fluid.layers.reduce_max(x, dim=-1) # [0.9, 0.7] fluid.layers.reduce_max(x, dim=1, keep_dim=True) # [[0.9], [0.7]] - # x is a Tensor variable with shape [2, 2, 2] and elements as below: + # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1.0, 2.0], [3.0, 4.0]], # [[5.0, 6.0], [7.0, 8.0]]] # Each example is followed by the correspending output tensor. - fluid.layers.reduce_max(x, dim=[1, 2]) # [4.0, 8.0] - fluid.layers.reduce_max(x, dim=[0, 1]) # [7.0, 8.0] + y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32') + fluid.layers.reduce_max(y, dim=[1, 2]) # [4.0, 8.0] + fluid.layers.reduce_max(y, dim=[0, 1]) # [7.0, 8.0] """ helper = LayerHelper('reduce_max', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) @@ -4674,21 +4794,24 @@ def reduce_min(input, dim=None, keep_dim=False, name=None): Examples: .. code-block:: python + import paddle.fluid as fluid # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the correspending output tensor. + x = fluid.layers.data(name='x', shape=[4, 2], dtype='float32') fluid.layers.reduce_min(x) # [0.1] fluid.layers.reduce_min(x, dim=0) # [0.1, 0.2, 0.5, 0.7] fluid.layers.reduce_min(x, dim=-1) # [0.2, 0.1] fluid.layers.reduce_min(x, dim=1, keep_dim=True) # [[0.2], [0.1]] - # x is a Tensor variable with shape [2, 2, 2] and elements as below: + # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1.0, 2.0], [3.0, 4.0]], # [[5.0, 6.0], [7.0, 8.0]]] # Each example is followed by the correspending output tensor. - fluid.layers.reduce_min(x, dim=[1, 2]) # [1.0, 5.0] - fluid.layers.reduce_min(x, dim=[0, 1]) # [1.0, 2.0] + y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32') + fluid.layers.reduce_min(y, dim=[1, 2]) # [1.0, 5.0] + fluid.layers.reduce_min(y, dim=[0, 1]) # [1.0, 2.0] """ helper = LayerHelper('reduce_min', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) @@ -4729,22 +4852,25 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None): Examples: .. code-block:: python + import paddle.fluid as fluid # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the correspending output tensor. + x = fluid.layers.data(name='x', shape=[4, 2], dtype='float32') fluid.layers.reduce_prod(x) # [0.0002268] fluid.layers.reduce_prod(x, dim=0) # [0.02, 0.06, 0.3, 0.63] fluid.layers.reduce_prod(x, dim=-1) # [0.027, 0.0084] fluid.layers.reduce_prod(x, dim=1, keep_dim=True) # [[0.027], [0.0084]] - # x is a Tensor variable with shape [2, 2, 2] and elements as below: + # y is a Tensor variable with shape [2, 2, 2] and elements as below: # [[[1.0, 2.0], [3.0, 4.0]], # [[5.0, 6.0], [7.0, 8.0]]] # Each example is followed by the correspending output tensor. - fluid.layers.reduce_prod(x, dim=[1, 2]) # [24.0, 1680.0] - fluid.layers.reduce_prod(x, dim=[0, 1]) # [105.0, 384.0] + y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32') + fluid.layers.reduce_prod(y, dim=[1, 2]) # [24.0, 1680.0] + fluid.layers.reduce_prod(y, dim=[0, 1]) # [105.0, 384.0] """ helper = LayerHelper('reduce_prod', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) @@ -4885,16 +5011,21 @@ def split(input, num_or_sections, dim=-1, name=None): Examples: .. code-block:: python - # x is a Tensor variable with shape [3, 9, 5]: - x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1) - x0.shape # [3, 3, 5] - x1.shape # [3, 3, 5] - x2.shape # [3, 3, 5] - x0, x1, x2 = fluid.layers.split( - x, num_or_sections=[2, 3, 4], dim=1) - x0.shape # [3, 2, 5] - x1.shape # [3, 3, 5] - x2.shape # [3, 4, 5] + import paddle.fluid as fluid + + # input is a variable which shape is [-1, 3, 9, 5] + input = fluid.layers.data( + name="input", shape=[3, 9, 5], dtype="float32") + + x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=2) + # x0.shape [-1, 3, 3, 5] + # x1.shape [-1, 3, 3, 5] + # x2.shape [-1, 3, 3, 5] + + x0, x1, x2 = fluid.layers.split(input, num_or_sections=3, dim=2) + # x0.shape [-1, 3, 2, 5] + # x1.shape [-1, 3, 3, 5] + # x2.shape [-1, 3, 4, 5] """ helper = LayerHelper('split', **locals()) input_shape = input.shape @@ -5024,25 +5155,29 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None): # Examples to clarify shapes of the inputs and output # x: [B, ..., M, K], y: [B, ..., K, N] - fluid.layers.matmul(x, y) # out: [B, ..., M, N] + # fluid.layers.matmul(x, y) # out: [B, ..., M, N] # x: [B, M, K], y: [B, K, N] - fluid.layers.matmul(x, y) # out: [B, M, N] + # fluid.layers.matmul(x, y) # out: [B, M, N] # x: [B, M, K], y: [K, N] - fluid.layers.matmul(x, y) # out: [B, M, N] + # fluid.layers.matmul(x, y) # out: [B, M, N] # x: [M, K], y: [K, N] - fluid.layers.matmul(x, y) # out: [M, N] + # fluid.layers.matmul(x, y) # out: [M, N] # x: [B, M, K], y: [K] - fluid.layers.matmul(x, y) # out: [B, M] + # fluid.layers.matmul(x, y) # out: [B, M] # x: [K], y: [K] - fluid.layers.matmul(x, y) # out: [1] + # fluid.layers.matmul(x, y) # out: [1] # x: [M], y: [N] - fluid.layers.matmul(x, y, True, True) # out: [M, N] + # fluid.layers.matmul(x, y, True, True) # out: [M, N] + + x = fluid.layers.data(name='x', shape=[2, 3], dtype='float32') + y = fluid.layers.data(name='y', shape=[3, 2], dtype='float32') + out = fluid.layers.matmul(x, y, True, True) """ def __check_input(x, y): @@ -5142,6 +5277,8 @@ def topk(input, k, name=None): Examples: .. code-block:: python + import paddle.fluid.layers as layers + input = layers.data(name="input", shape=[13, 11], dtype='float32') top5_values, top5_indices = layers.topk(input, k=5) """ helper = LayerHelper("top_k", **locals()) @@ -5166,7 +5303,7 @@ def topk(input, k, name=None): def edit_distance(input, label, normalized=True, ignored_tokens=None): """ - EditDistance operator computes the edit distances between a batch of + Edit distance operator computes the edit distances between a batch of hypothesis strings and their references. Edit distance, also called Levenshtein distance, measures how dissimilar two strings are by counting the minimum number of operations to transform one string into anthor. @@ -5202,9 +5339,28 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None): Examples: .. code-block:: python - x = fluid.layers.data(name='x', shape=[1], dtype='float32') - y = fluid.layers.data(name='y', shape=[1], dtype='float32') - cost = fluid.layers.edit_distance(input=x,label=y) + import paddle.fluid as fluid + x = fluid.layers.data(name='x', shape=[1], dtype='int64') + y = fluid.layers.data(name='y', shape=[1], dtype='int64') + cost, _ = fluid.layers.edit_distance(input=x, label=y) + + cpu = fluid.core.CPUPlace() + exe = fluid.Executor(cpu) + exe.run(fluid.default_startup_program()) + + import numpy + x_ = numpy.random.randint(5, size=(2, 1)).astype('int64') + y_ = numpy.random.randint(5, size=(2, 1)).astype('int64') + + print(x_) + print(y_) + + x = fluid.create_lod_tensor(x_, [[2]], cpu) + y = fluid.create_lod_tensor(y_, [[2]], cpu) + + outs = exe.run(feed={'x':x, 'y':y}, fetch_list=[cost.name]) + + print(outs) """ helper = LayerHelper("edit_distance", **locals()) @@ -5359,8 +5515,11 @@ def warpctc(input, label, blank=0, norm_by_times=False, use_cudnn=False): .. code-block:: python - label = fluid.layers.data(shape=[11, 8], dtype='float32', lod_level=1) - predict = fluid.layers.data(shape=[11, 1], dtype='float32') + import paddle.fluid as fluid + label = fluid.layers.data(name='label', shape=[11, 8], + dtype='float32', lod_level=1) + predict = fluid.layers.data(name='predict', shape=[11, 1], + dtype='float32') cost = fluid.layers.warpctc(input=predict, label=label) """ @@ -5426,8 +5585,9 @@ def sequence_reshape(input, new_dim): Examples: .. code-block:: python - x = fluid.layers.data(shape=[5, 20], dtype='float32', lod_level=1) - x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10) + import paddle.fluid as fluid + x = fluid.layers.data(name='x', shape=[2, 6], append_batch_size=False, dtype='float32', lod_level=1) + x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=4) """ assert not in_dygraph_mode(), ( "sequence layer is not supported in dygraph mode yet.") @@ -5729,6 +5889,7 @@ def hsigmoid(input, .. code-block:: python + import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[2], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='int64') out = fluid.layers.hsigmoid(input=x, label=y, num_classes=6) @@ -5834,15 +5995,16 @@ def transpose(x, perm, name=None): # use append_batch_size=False to avoid prepending extra # batch size in shape + import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32', append_batch_size=False) - x_transposed = layers.transpose(x, perm=[1, 0, 2]) + x_transposed = fluid.layers.transpose(x, perm=[1, 0, 2]) """ if len(perm) != len(x.shape): raise ValueError( "Input(perm) is the permutation of dimensions of Input(input). " - "It's length shoud be equal to Input(input)'s rank.") + "Its length should be equal to Input(input)'s rank.") for idx, dim in enumerate(perm): if dim >= len(x.shape): raise ValueError( @@ -5972,8 +6134,12 @@ def im2sequence(input, .. code-block:: python + import paddle.fluid as fluid + data = fluid.layers.data(name='data', shape=[3, 32, 32], + dtype='float32') output = fluid.layers.im2sequence( - input=layer, stride=[1, 1], filter_size=[2, 2]) + input=data, stride=[1, 1], filter_size=[2, 2]) + """ assert not in_dygraph_mode(), ( @@ -5989,7 +6155,7 @@ def im2sequence(input, padding.append(padding[0]) padding.append(padding[1]) inputs = {"X": input} - attrs = {"kernels": filter_size, "strides": stride, "padding": padding} + attrs = {"kernels": filter_size, "strides": stride, "paddings": padding} if input_image_size: if isinstance(out_stride, int): out_stride = [out_stride, out_stride] @@ -6296,11 +6462,13 @@ def sampled_softmax_with_cross_entropy(logits, Examples: .. code-block:: python - logits = fluid.layers.data(name='data', shape=[256], dtype='float32') + import paddle.fluid as fluid + + input = fluid.layers.data(name='data', shape=[256], dtype='float32') label = fluid.layers.data(name='label', shape=[5], dtype='int64') - fc = fluid.layers.fc(input=data, size=100) + fc = fluid.layers.fc(input=input, size=100) out = fluid.layers.sampled_softmax_with_cross_entropy( - logits=fc, label=label, num_samples=25) + logits=fc, label=label, num_samples=25) """ helper = LayerHelper('sample_logits', **locals()) samples = helper.create_variable_for_type_inference(dtype='int64') @@ -6431,11 +6599,25 @@ def one_hot(input, depth): one_hot_label = fluid.layers.one_hot(input=label, depth=10) """ helper = LayerHelper("one_hot", **locals()) + one_hot_out = helper.create_variable_for_type_inference(dtype='float32') + + if in_dygraph_mode(): + inputs = {'X': input} + attrs = {'depth': depth} + else: + if not isinstance(depth, Variable): + # user attribute + inputs = {'X': input} + attrs = {'depth': depth} + else: + depth.stop_gradient = True + inputs = {'X': input, 'depth_tensor': depth} + attrs = {} helper.append_op( type="one_hot", - inputs={'X': input}, - attrs={'depth': depth}, + inputs=inputs, + attrs=attrs, outputs={'Out': one_hot_out}, stop_gradient=True) return one_hot_out @@ -6557,6 +6739,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None): if not (isinstance(shape, list) or isinstance(shape, tuple)): raise ValueError("Input shape must be a python list or tuple.") + inputs = {"X": x} if isinstance(actual_shape, Variable): inputs["Shape"] = actual_shape @@ -6565,7 +6748,12 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None): # Validate the shape unk_dim_idx = -1 + contain_var = False for dim_idx, dim_size in enumerate(shape): + if isinstance(dim_size, Variable): + contain_var = True + continue + if dim_size == -1: assert unk_dim_idx == -1, ( "Only one dimension in shape can be unknown.") @@ -6579,13 +6767,35 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None): "except one unknown dimension.") helper = LayerHelper("reshape2", **locals()) + if in_dygraph_mode(): + inputs = {'X': x} + attrs = {'shape': shape} + else: + if contain_var: + new_shape_tensor = [] + for dim in shape: + if isinstance(dim, Variable): + dim.stop_gradient = True + new_shape_tensor.append(dim) + else: + assert (isinstance(dim, int)) + temp_out = helper.create_variable_for_type_inference( + 'int32') + fill_constant( + [1], 'int32', dim, force_cpu=True, out=temp_out) + new_shape_tensor.append(temp_out) + inputs['ShapeTensor'] = new_shape_tensor + attrs = {} + + else: + attrs = {'shape': shape} out = x if inplace else helper.create_variable_for_type_inference( dtype=x.dtype) x_shape = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type="reshape2", inputs=inputs, - attrs={"shape": shape}, + attrs=attrs, outputs={"Out": out, "XShape": x_shape}) @@ -6632,8 +6842,9 @@ def squeeze(input, axes, name=None): Examples: .. code-block:: python + import paddle.fluid.layers as layers x = layers.data(name='x', shape=[5, 1, 10]) - y = layers.sequeeze(input=x, axes=[1]) + y = layers.squeeze(input=x, axes=[1]) """ assert not in_dygraph_mode(), ( "squeeze layer is not supported in dygraph mode yet.") @@ -6674,8 +6885,9 @@ def unsqueeze(input, axes, name=None): Examples: .. code-block:: python - x = layers.data(name='x', shape=[5, 10]) - y = layers.unsequeeze(input=x, axes=[1]) + import paddle.fluid as fluid + x = fluid.layers.data(name='x', shape=[5, 10]) + y = fluid.layers.unsqueeze(input=x, axes=[1]) """ helper = LayerHelper("unsqueeze", **locals()) out = helper.create_variable_for_type_inference(dtype=input.dtype) @@ -6764,9 +6976,9 @@ def lod_reset(x, y=None, target_lod=None): Examples: .. code-block:: python - x = layers.data(name='x', shape=[10]) - y = layers.data(name='y', shape=[10, 20], lod_level=2) - out = layers.lod_reset(x=x, y=y) + x = fluid.layers.data(name='x', shape=[10]) + y = fluid.layers.data(name='y', shape=[10, 20], lod_level=2) + out = fluid.layers.lod_reset(x=x, y=y) """ helper = LayerHelper("lod_reset", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) @@ -7044,6 +7256,8 @@ def label_smooth(label, Examples: .. code-block:: python + + import paddle.fluid.layers as layers label = layers.data(name="label", shape=[1], dtype="float32") one_hot_label = layers.one_hot(input=label, depth=10) @@ -7082,7 +7296,19 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0): Examples: .. code-block:: python - pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0) + import paddle.fluid as fluid + + x = fluid.layers.data( + name='x', shape=[8, 112, 112], dtype='float32') + rois = fluid.layers.data( + name='roi', shape=[4], lod_level=1, dtype='float32') + pool_out = fluid.layers.roi_pool( + input=x, + rois=rois, + pooled_height=7, + pooled_width=7, + spatial_scale=1.0) + """ helper = LayerHelper('roi_pool', **locals()) dtype = helper.input_dtype() @@ -7656,7 +7882,7 @@ def image_resize_short(input, out_short_len, resample='BILINEAR'): return image_resize(input=input, out_shape=out_shape, resample=resample) -def gather(input, index): +def gather(input, index, overwrite=True): """ **Gather Layer** @@ -7687,6 +7913,12 @@ def gather(input, index): Args: input (Variable): The source input with rank>=1. index (Variable): The index input with rank=1. + overwrite (bool): The mode that updating the grad when has same index. + If True, use the overwrite mode to update the grad of the same index, + if False, use the accumulate mode to update the grad of the same index. + Default value is True. + + Returns: output (Variable): The output is a tensor with the same rank as input. @@ -7706,11 +7938,12 @@ def gather(input, index): type="gather", inputs={"X": input, "Index": index}, - outputs={"Out": out}) + outputs={"Out": out}, + attrs={'overwrite': overwrite}) return out -def scatter(input, index, updates, name=None): +def scatter(input, index, updates, name=None, overwrite=True): """ **Scatter Layer** @@ -7728,6 +7961,10 @@ def scatter(input, index, updates, name=None): int32 or int64 as it is used as indexes. updates (Variable): The updated value of scatter op. name (str|None): The output variable name. Default None. + overwrite (bool): The mode that updating the output when has same index. + If True, use the overwrite mode to update the output of the same index, + if False, use the accumulate mode to update the output of the same index. + Default value is True.You can set overwrite=False to implement scatter_add. Returns: output (Variable): The output is a tensor with the same shape as input. @@ -7736,8 +7973,13 @@ def scatter(input, index, updates, name=None): .. code-block:: python - output = fluid.layers.scatter(input, index, updates) + import paddle.fluid as fluid + input = fluid.layers.data(name='data', shape=[3, 5, 9], dtype='float32', append_batch_size=False) + index = fluid.layers.data(name='index', shape=[3], dtype='int64', append_batch_size=False) + updates = fluid.layers.data(name='update', shape=[3, 5, 9], dtype='float32', append_batch_size=False) + + output = fluid.layers.scatter(input, index, updates) """ helper = LayerHelper('scatter', **locals()) dtype = helper.input_dtype() @@ -7747,6 +7989,7 @@ def scatter(input, index, updates, name=None): inputs={"X": input, "Ids": index, "Updates": updates}, + attrs={'overwrite': overwrite}, outputs={"Out": out}) return out @@ -7800,7 +8043,12 @@ def sequence_scatter(input, index, updates, name=None): Examples: .. code-block:: python + + import paddle.fluid.layers as layers + input = layers.data( name="x", shape=[3, 6], append_batch_size=False, dtype='float32' ) + index = layers.data( name='index', shape=[1], dtype='int32') + updates = layers.data( name='updates', shape=[1], dtype='float32') output = fluid.layers.sequence_scatter(input, index, updates) """ @@ -7880,6 +8128,7 @@ def log(x, name=None): .. code-block:: python + x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32") output = fluid.layers.log(x) """ helper = LayerHelper('log', **locals()) @@ -7946,8 +8195,12 @@ def selu(x, scale=None, alpha=None, name=None): Examples: .. code-block:: python - - output = fluid.layers.selu(x) + + import paddle.fluid as fluid + + input = fluid.layers.data( + name="input", shape=[3, 9, 5], dtype="float32") + output = fluid.layers.selu(input) """ helper = LayerHelper('selu', **locals()) dtype = helper.input_dtype(input_param_name='x') @@ -7997,7 +8250,11 @@ def mean_iou(input, label, num_classes): .. code-block:: python - iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes) + import paddle.fluid as fluid + predict = fluid.layers.data(name='predict', shape=[3, 32, 32]) + label = fluid.layers.data(name='label', shape=[1]) + iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, + num_classes=5) """ helper = LayerHelper('mean_iou', **locals()) dtype = helper.input_dtype() @@ -8282,9 +8539,9 @@ def rank_loss(label, left, right, name=None): .. code-block:: python - label = fluid.layers.data(name="label", shape=[4, 1], dtype="float32") - left = fluid.layers.data(name="left", shape=[4, 1], dtype="float32") - right = fluid.layers.data(name="right", shape=[4, 1], dtype="float32") + label = fluid.layers.data(name="label", shape=[-1, 1], dtype="float32") + left = fluid.layers.data(name="left", shape=[-1, 1], dtype="float32") + right = fluid.layers.data(name="right", shape=[-1, 1], dtype="float32") out = fluid.layers.rank_loss(label, left, right) """ @@ -8431,8 +8688,11 @@ def pad2d(input, Examples: .. code-block:: python - data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32') - result = fluid.layers.pad2d(input=data, padding=[1,2,3,4], mode='reflect') + import paddle.fluid as fluid + data = fluid.layers.data(name='data', shape=[3, 32, 32], + dtype='float32') + result = fluid.layers.pad2d(input=data, paddings=[1, 2, 3, 4], + mode='reflect') """ helper = LayerHelper('pad2d', **locals()) @@ -8530,6 +8790,7 @@ def pow(x, factor=1.0, name=None): .. code-block:: python + import paddle.fluid as fluid x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32") y = fluid.layers.pow(x, factor=2.0) """ @@ -8593,6 +8854,7 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None): .. code-block:: python + import paddle.fluid as fluid x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32") y = fluid.layers.hard_sigmoid(x, slope=0.3, offset=0.8) """ @@ -8644,14 +8906,19 @@ def prelu(x, mode, param_attr=None, name=None): .. math:: y = \max(0, x) + \\alpha * \min(0, x) + There are three modes for the activation: + + .. code-block:: text + + all: All elements share same alpha. + channel: Elements in same channel share same alpha. + element: All elements do not share alpha. Each element has its own alpha. + Args: x (Variable): The input tensor. + mode (string): The mode for weight sharing. param_attr(ParamAttr|None): The parameter attribute for the learnable - weight (alpha). - mode (string): The mode for weight sharing. It supports all, channel - and element. all: all elements share same weight - channel:elements in a channel share same weight - element:each element has a weight + weight (alpha), it can be create by ParamAttr. name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. @@ -8662,9 +8929,13 @@ def prelu(x, mode, param_attr=None, name=None): .. code-block:: python - x = fluid.layers.data(name="x", shape=[10,10], dtype="float32") + import paddle.fluid as fluid + from paddle.fluid.param_attr import ParamAttr + x = fluid.layers.data(name="x", shape=[5,10,10], dtype="float32") mode = 'channel' - output = fluid.layers.prelu(x,mode) + output = fluid.layers.prelu( + x,mode,param_attr=ParamAttr(name='alpha')) + """ helper = LayerHelper('prelu', **locals()) if mode not in ['all', 'channel', 'element']: @@ -8765,9 +9036,11 @@ def soft_relu(x, threshold=40.0, name=None): Examples: - .. code-block:: python - - x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32") + .. code-block:: python + + import paddle.fluid as fluid + + x = fluid.layers.data(name="x", shape=[3,16,16], dtype="float32") y = fluid.layers.soft_relu(x, threshold=20.0) """ helper = LayerHelper('soft_relu', **locals()) @@ -8894,7 +9167,7 @@ def sequence_enumerate(input, win_size, pad_value=0, name=None): Examples: .. code-block:: python - x = fluid.layers.data(shape[30, 1], dtype='int32', lod_level=1) + x = fluid.layers.data(shape[-1, 1], dtype='int32', lod_level=1) out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0) """ assert not in_dygraph_mode(), ( @@ -8937,6 +9210,14 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None): Returns: Variable: The output sequence mask. + Examples: + .. code-block:: python + + import paddle.fluid.layers as layers + + x = fluid.layers.data(name='x', shape=[10], dtype='float32', lod_level=1) + mask = layers.sequence_mask(x=x) + """ assert not in_dygraph_mode(), ( "sequence layer is not supported in dygraph mode yet.") @@ -9020,6 +9301,14 @@ def stack(x, axis=0): Returns: Variable: The stacked variable. + Examples: + .. code-block:: python + + import paddle.fluid.layers as layers + x1 = layers.data(name='x1', shape=[1, 2], dtype='int32') + x2 = layers.data(name='x2', shape=[1, 2], dtype='int32') + data = layers.stack([x1,x2]) + """ helper = LayerHelper('stack', **locals()) @@ -9055,6 +9344,12 @@ def unstack(x, axis=0, num=None): Returns: list(Variable): The unstacked variables. + Examples: + .. code-block:: python + + import paddle.fluid as fluid + x = fluid.layers.data(name='x', shape=[5, 10], dtype='float32') + y = fluid.layers.unstack(x, axis=1) """ helper = LayerHelper('unstack', **locals()) @@ -9119,11 +9414,39 @@ def expand(x, expand_times, name=None): helper = LayerHelper('expand', input=x, **locals()) dtype = helper.input_dtype(input_param_name='x') out = helper.create_variable_for_type_inference(dtype) + # check expand_times have tensor + + if in_dygraph_mode(): + inputs = {'X': x} + attrs = {'expand_times': expand_times} + else: + + def contain_tensor(expand_times): + for ele in expand_times: + if isinstance(ele, Variable): + return True + return False + + if contain_tensor(expand_times): + new_expand_times = [] + for ele in expand_times: + if isinstance(ele, Variable): + ele.stop_gradient = True + new_expand_times.append(ele) + else: + assert (isinstance(ele, int)) + temp_out = helper.create_variable_for_type_inference(dtype) + fill_constant( + [1], 'int32', ele, force_cpu=True, out=temp_out) + new_expand_times.append(temp_out) + inputs = {'X': x, 'expand_times_tensor': new_expand_times} + attrs = {} + else: + inputs = {'X': x} + attrs = {'expand_times': expand_times} + helper.append_op( - type='expand', - inputs={'X': x}, - outputs={'Out': out}, - attrs={'expand_times': expand_times}) + type='expand', inputs=inputs, outputs={'Out': out}, attrs=attrs) return out @@ -9157,6 +9480,8 @@ def uniform_random_batch_size_like(input, Examples: .. code-block:: python + import paddle.fluid.layers as layers + input = layers.data(name="input", shape=[13, 11], dtype='float32') out = layers.uniform_random_batch_size_like(input, [-1, 11]) """ @@ -9199,6 +9524,7 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'): Examples: .. code-block:: python + import paddle.fluid.layers as layers out = layers.gaussian_random(shape=[20, 30]) """ @@ -9328,8 +9654,10 @@ def sum(x): Examples: .. code-block:: python - input = layers.data(name="input", shape=[13, 11], dtype='float32') - out = layers.sum(input) + import paddle.fluid.layers as layers + input0 = layers.data(name="input0", shape=[13, 11], dtype='float32') + input1 = layers.data(name="input1", shape=[13, 11], dtype='float32') + out = layers.sum([input0,input1]) """ helper = LayerHelper('sum', **locals()) @@ -9347,8 +9675,39 @@ def sum(x): @templatedoc() def slice(input, axes, starts, ends): """ - ${comment} + Slice Operator. + + Produces a slice of the input tensor along multiple axes. Similar to numpy: + https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html + Slice uses `axes`, `starts` and `ends` attributes to specify the start and + end dimension for each axis in the list of axes, it uses this information + to slice the input data tensor. If a negative value is passed for any of + the start or end indices, it represents number of elements before the end + of that dimension. If the value passed to start or end is larger than + the n (the number of elements in this dimension), it represents n. + For slicing to the end of a dimension with unknown size, it is recommended + to pass in INT_MAX. The size of axes must be equal to starts\' and ends\'. + Following examples will explain how slice works: + + .. code-block:: text + Case1: + Given: + data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] + axes = [0, 1] + starts = [1, 0] + ends = [2, 3] + Then: + result = [ [5, 6, 7], ] + + Case2: + Given: + data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] + axes = [0, 1] + starts = [0, 1] + ends = [-1, 1000] + Then: + result = [ [2, 3, 4], ] Args: input (Variable): ${input_comment}. axes (List): ${axes_comment} @@ -9361,14 +9720,16 @@ def slice(input, axes, starts, ends): Examples: .. code-block:: python + import paddle.fluid as fluid + starts = [1, 0, 2] ends = [3, 3, 4] axes = [0, 1, 2] - input = layers.data( + input = fluid.layers.data( name="input", shape=[3, 4, 5, 6], dtype='float32') - out = layers.slice(input, axes=axes, starts=starts, ends=ends) + out = fluid.layers.slice(input, axes=axes, starts=starts, ends=ends) """ helper = LayerHelper('slice', **locals()) @@ -9400,9 +9761,11 @@ def shape(input): Examples: .. code-block:: python - input = layers.data( + import paddle.fluid as fluid + + input = fluid.layers.data( name="input", shape=[3, 100, 100], dtype="float32") - out = layers.shape(input) + out = fluid.layers.shape(input) """ helper = LayerHelper('shape', **locals()) @@ -9483,6 +9846,14 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None): Returns: out(${out_type}): ${out_comment} + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + x = fluid.layers.data(name="X", shape=[1, 2, 5, 5], dtype='float32') + y = fluid.layers.scale(x, scale = 2.0, bias = 1.0) """ helper = LayerHelper('scale', **locals()) @@ -9558,6 +9929,43 @@ for func in [ "act (basestring|None): Activation applied to the output.", "name (basestring|None): Name of the output." ]) + func.__doc__ = func.__doc__ + """ + +Examples: + .. code-block:: python + + import paddle.fluid as fluid + # example 1: shape(x) = (2, 3, 4, 5), shape(y) = (2, 3, 4, 5) + x0 = fluid.layers.data(name="x0", shape=[2, 3, 4, 5], dtype='float32') + y0 = fluid.layers.data(name="y0", shape=[2, 3, 4, 5], dtype='float32') + z0 = fluid.layers.%s(x0, y0) + + # example 2: shape(X) = (2, 3, 4, 5), shape(Y) = (5) + x1 = fluid.layers.data(name="x1", shape=[2, 3, 4, 5], dtype='float32') + y1 = fluid.layers.data(name="y1", shape=[5], dtype='float32') + z1 = fluid.layers.%s(x1, y1) + + # example 3: shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5), with axis=-1(default) or axis=2 + x2 = fluid.layers.data(name="x2", shape=[2, 3, 4, 5], dtype='float32') + y2 = fluid.layers.data(name="y2", shape=[4, 5], dtype='float32') + z2 = fluid.layers.%s(x2, y2, axis=2) + + # example 4: shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 + x3 = fluid.layers.data(name="x3", shape=[2, 3, 4, 5], dtype='float32') + y3 = fluid.layers.data(name="y3", shape=[3, 4], dtype='float32') + z3 = fluid.layers.%s(x3, y3, axis=1) + + # example 5: shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0 + x4 = fluid.layers.data(name="x4", shape=[2, 3, 4, 5], dtype='float32') + y4 = fluid.layers.data(name="y4", shape=[2], dtype='float32') + z4 = fluid.layers.%s(x4, y4, axis=0) + + # example 6: shape(X) = (2, 3, 4, 5), shape(Y) = (2, 1), with axis=0 + x5 = fluid.layers.data(name="x5", shape=[2, 3, 4, 5], dtype='float32') + y5 = fluid.layers.data(name="y5", shape=[2], dtype='float32') + z5 = fluid.layers.%s(x5, y5, axis=0) + """ % (func.__name__, func.__name__, func.__name__, func.__name__, + func.__name__, func.__name__) def _logical_op(op_name, x, y, out=None, name=None, binary_op=True): @@ -9718,7 +10126,8 @@ def clip(x, min, max, name=None): helper = LayerHelper("clip", **locals()) if name is None: - name = unique_name.generate(".".join([helper.name, 'tmp'])) + name = unique_name.generate_with_ignorable_key(".".join( + [helper.name, 'tmp'])) out = helper.create_variable( type=x.type, name=name, dtype=x.dtype, persistable=False) @@ -9757,7 +10166,8 @@ def clip_by_norm(x, max_norm, name=None): helper = LayerHelper("clip_by_norm", **locals()) if name is None: - name = unique_name.generate(".".join([helper.name, 'tmp'])) + name = unique_name.generate_with_ignorable_key(".".join( + [helper.name, 'tmp'])) out = helper.create_variable( type=x.type, name=name, dtype=x.dtype, persistable=False) @@ -9782,6 +10192,13 @@ def mean(x, name=None): Returns: out(${out_type}): ${out_comment} + + Examples: + .. code-block:: python + + input = fluid.layers.data( + name='data', shape=[2, 3], dtype='float32') + mean = fluid.layers.mean(input) """ helper = LayerHelper("mean", **locals()) @@ -9809,6 +10226,15 @@ def merge_selected_rows(x, name=None): Returns: out(${out_type}): ${out_comment} + + Examples: + .. code-block:: python + + b = fluid.default_main_program().global_block() + var = b.create_var( + name="X", dtype="float32", persistable=True, + type=fluid.core.VarDesc.VarType.SELECTED_ROWS) + y = fluid.layers.merge_selected_rows(var) """ helper = LayerHelper("merge_selected_rows", **locals()) @@ -9835,6 +10261,18 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None): Returns: out(${out_type}): ${out_comment} + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + dataX = fluid.layers.data(name="dataX", append_batch_size = False, shape=[2, 5], dtype="float32") + dataY = fluid.layers.data(name="dataY", append_batch_size = False, shape=[5, 3], dtype="float32") + output = fluid.layers.mul(dataX, dataY, + x_num_col_dims = 1, + y_num_col_dims = 1) + + """ helper = LayerHelper("mul", **locals()) @@ -9981,6 +10419,9 @@ def space_to_depth(x, blocksize, name=None): Examples: .. code-block:: python + + import paddle.fluid as fluid + import numpy as np data = fluid.layers.data( name='data', shape=[1, 4, 2, 2], dtype='float32', append_batch_size=False) @@ -9992,6 +10433,7 @@ def space_to_depth(x, blocksize, name=None): out_main = exe.run(fluid.default_main_program(), feed={'data': data_np}, fetch_list=[space_to_depthed]) + """ helper = LayerHelper("space_to_depth", **locals()) @@ -10025,6 +10467,13 @@ def sequence_reverse(x, name=None): Returns: out(${y_type}): ${y_comment} + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + x = fluid.layers.data(name='x', shape=[2, 6], dtype='float32') + x_reversed = fluid.layers.sequence_reverse(x) """ assert not in_dygraph_mode(), ( "sequence layer is not supported in dygraph mode yet.") @@ -10071,6 +10520,20 @@ def affine_channel(x, Returns: out (Variable): A tensor of the same shape and data layout with x. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + data = fluid.layers.data(name='data', shape=[3, 32, 32], + dtype='float32') + input_scale = fluid.layers.create_parameter(shape=[3], + dtype="float32") + input_bias = fluid.layers.create_parameter(shape=[3], + dtype="float32") + out = fluid.layers.affine_channel(data,scale=input_scale, + bias=input_bias) + """ helper = LayerHelper("affine_channel", **locals()) @@ -10219,8 +10682,8 @@ def hash(input, hash_size, num_hash=1, name=None): # shape [2, 2] input.data = [ - [[1], [2]], - [[3], [4]], + [[1, 2], + [3, 4]], ] input.lod = [[0, 2]] @@ -10237,8 +10700,8 @@ def hash(input, hash_size, num_hash=1, name=None): # shape [2, 4] output.data = [ - [[9662], [9217], [1129], [8487]], - [[8310], [1327], [1654], [4567]], + [[9662, 9217, 1129, 8487], + [8310, 1327, 1654, 4567]], ] output.lod = [[0, 2]] @@ -10257,8 +10720,24 @@ def hash(input, hash_size, num_hash=1, name=None): Examples: .. code-block:: python - x = fluid.layers.data(name="x", shape=[1], dtype='int32', lod_level=1) - out = fluid.layers.hash(input=x, num_hash=4, hash_size=1000) + import paddle.fluid as fluid + import paddle.fluid.layers as layers + import numpy as np + + titles = fluid.layers.data(name='titles', shape=[1], dtype='int32', lod_level=1) + hash_r = fluid.layers.hash(name='hash_x', input=titles, num_hash=1, hash_size=1000) + + place = fluid.core.CPUPlace() + exece = fluid.Executor(place) + exece.run(fluid.default_startup_program()) + + # Init Tensor + tensor = fluid.core.LoDTensor() + tensor.set(np.random.randint(0, 10, (3, 1)).astype("int32"), place) + # Set LoD + tensor.set_recursive_sequence_lengths([[1, 1, 1]]) + + out = exece.run(feed={'titles': tensor}, fetch_list=[hash_r], return_numpy=False) """ helper = LayerHelper('hash', **locals()) out = helper.create_variable_for_type_inference( @@ -10336,9 +10815,11 @@ def grid_sampler(x, grid, name=None): .. code-block:: python - x = fluid.layers.data(name='x', shape=[3, 10, 32, 32], dtype='float32') - theta = fluid.layers.data(name='theta', shape=[3, 2, 3], dtype='float32') - grid = fluid.layers.affine_grid(input=theta, size=[3, 10, 32, 32]}) + import paddle.fluid as fluid + + x = fluid.layers.data(name='x', shape=[10, 32, 32], dtype='float32') + theta = fluid.layers.data(name='theta', shape=[2, 3], dtype='float32') + grid = fluid.layers.affine_grid(theta=theta, out_shape=[3, 10, 32, 32]) out = fluid.layers.grid_sampler(x=x, grid=grid) """ @@ -10432,8 +10913,16 @@ def teacher_student_sigmoid_loss(input, Examples: .. code-block:: python + + import paddle.fluid as fluid + batch_size = 64 + label = fluid.layers.data( + name="label", shape=[batch_size, 1], dtype="int64", append_batch_size=False) + similarity = fluid.layers.data( + name="similarity", shape=[batch_size, 1], dtype="float32", append_batch_size=False) cost = fluid.layers.teacher_student_sigmoid_loss(input=similarity, label=label) + """ helper = LayerHelper('teacher_student_sigmoid_loss', **locals()) out = helper.create_variable(dtype=input.dtype) @@ -10477,7 +10966,15 @@ def add_position_encoding(input, alpha, beta, name=None): Examples: .. code-block:: python - position_tensor = fluid.layers.add_position_encoding(input=tensor) + import paddle.fluid as fluid + + tensor = fluid.layers.data( + name='tensor', + shape=[32, 64, 512], + dtype='float32', + append_batch_size=False) + position_tensor = fluid.layers.add_position_encoding( + input=tensor, alpha=1.0, beta=1.0) """ helper = LayerHelper('add_position_encoding', **locals()) @@ -10579,6 +11076,14 @@ def get_tensor_from_selected_rows(x, name=None): Returns: out(${out_type}): ${out_comment} + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + b = fluid.default_main_program().global_block() + input = b.create_var(name="X", dtype="float32", persistable=True, type=fluid.core.VarDesc.VarType.SELECTED_ROWS) + out = fluid.layers.get_tensor_from_selected_rows(input) """ helper = LayerHelper('get_tensor_from_selected_rows', **locals()) @@ -11016,8 +11521,15 @@ def huber_loss(input, label, delta): Examples: .. code-block:: python - predictions = fluid.layers.softmax(x) - loss = fluid.layers.huber_loss(input=predictions, label=label, 1.0) + import paddle.fluid as fluid + + x = fluid.layers.data(name='x', shape=[13], dtype='float32') + predict = fluid.layers.fc(input=x, size=1) + label = fluid.layers.data( + name='label', shape=[1], dtype='float32') + loss = fluid.layers.huber_loss( + input=predict, label=label, delta=1.0) + """ helper = LayerHelper('huber_loss', **locals()) residual = helper.create_variable_for_type_inference( @@ -11281,8 +11793,12 @@ def fsp_matrix(x, y): .. code-block:: python - feature_map_0 = fluid.layers.conv2d(x) - feature_map_1 = fluid.layers.conv2d(feature_map_0) + import paddle.fluid as fluid + data = fluid.layers.data(name='data', shape=[3, 32, 32]) + feature_map_0 = fluid.layers.conv2d(data, num_filters=2, + filter_size=3) + feature_map_1 = fluid.layers.conv2d(feature_map_0, num_filters=2, + filter_size=1) loss = fluid.layers.fsp_matrix(feature_map_0, feature_map_1) """ @@ -11311,7 +11827,7 @@ def continuous_value_model(input, cvm, use_cvm=True): cvm (Variable): a 2-D Tensor with shape [N x 2], where N is the batch size, 2 is show and click. use_cvm (bool): use cvm or not. if use cvm, the output dim is the same as input if don't use cvm, the output dim is input dim - 2(remove show and click) - (cvm op is a customized op, which input is a sequence has embedd_with_cvm default, so we need an op named cvm to decided whever use it or not.) + (cvm op is a customized op, which input is a sequence has embed_with_cvm default, so we need an op named cvm to decided whever use it or not.) Returns: @@ -11377,3 +11893,430 @@ def where(condition): helper.append_op( type='where', inputs={'Condition': condition}, outputs={'Out': [out]}) return out + + +def sign(x): + """ + **sign** + + This function returns sign of every element in `x`: 1 for positive, -1 for negative and 0 for zero. + + Args: + x(Variable|numpy.ndarray): The input tensor. + + Returns: + Variable: The output sign tensor with identical shape and dtype to `x`. + + Examples: + .. code-block:: python + + # [1, 0, -1] + data = fluid.layers.sign(np.array([3, 0, -2])) + """ + + helper = LayerHelper("sign", **locals()) + + if not isinstance(x, Variable): + x = assign(x) + + out = helper.create_variable_for_type_inference(dtype=x.dtype) + + helper.append_op(type='sign', inputs={'X': [x]}, outputs={'Out': [out]}) + + return out + + +def deformable_conv(input, + offset, + mask, + num_filters, + filter_size, + stride=1, + padding=0, + dilation=1, + groups=None, + deformable_groups=None, + im2col_step=None, + param_attr=None, + bias_attr=None, + name=None): + """ + **Deformable Convolution Layer** + + Compute 2-D deformable convolution on 4-D input. + Given input image x, output feature map y, the deformable convolution operation can be expressed as follow: + + .. math:: + + y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k) * \Delta m_k} + + Where :math:`\Delta p_k` and :math:`\Delta m_k` are the learnable offset and modulation scalar for the k-th location, respectively. + Refer to `Deformable ConvNets v2: More Deformable, Better Results + `_ . + + Example: + - Input: + + Input shape: :math:`(N, C_{in}, H_{in}, W_{in})` + + Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)` + + Offset shape: :math:`(N, 2 * deformable\_groups * H_f * H_w, H_{in}, W_{in})` + + Mask shape: :math:`(N, deformable\_groups * H_f * H_w, H_{in}, W_{in})` + + - Output: + + Output shape: :math:`(N, C_{out}, H_{out}, W_{out})` + + Where + + .. math:: + + H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\ + W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1 + + Args: + input (Variable): The input image with [N, C, H, W] format. + offset (Variable): The input coord offset of deformable convolution layer. + Mask (Variable): The input mask of deformable covolution layer. + num_filters(int): The number of filter. It is as same as the output + image channel. + filter_size (int|tuple|None): The filter size. If filter_size is a tuple, + it must contain two integers, (filter_size_H, filter_size_W). + Otherwise, the filter will be a square. + stride (int|tuple): The stride size. If stride is a tuple, it must + contain two integers, (stride_H, stride_W). Otherwise, the + stride_H = stride_W = stride. Default: stride = 1. + padding (int|tuple): The padding size. If padding is a tuple, it must + contain two integers, (padding_H, padding_W). Otherwise, the + padding_H = padding_W = padding. Default: padding = 0. + dilation (int|tuple): The dilation size. If dilation is a tuple, it must + contain two integers, (dilation_H, dilation_W). Otherwise, the + dilation_H = dilation_W = dilation. Default: dilation = 1. + groups (int): The groups number of the deformable conv layer. According to + grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, + the first half of the filters is only connected to the first half + of the input channels, while the second half of the filters is only + connected to the second half of the input channels. Default: groups=1. + deformable_groups (int): The number of deformable group partitions. + Default: deformable_groups = 1. + im2col_step (int): Maximum number of images per im2col computation; + The total batch size should be divisable by this value or smaller + than this value; if you face out of memory problem, you can try + to use a smaller value here. + Default: im2col_step = 64. + param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights + of deformable conv. If it is set to None or one attribute of ParamAttr, + deformable conv will create ParamAttr as param_attr. + If the Initializer of the param_attr is not set, the parameter is + initialized with :math:`Normal(0.0, std)`, and the + :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None. + bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of + deformable conv layer. If it is set to False, no bias will be added + to the output units. If it is set to None or one attribute of ParamAttr, conv2d + will create ParamAttr as bias_attr. If the Initializer of the bias_attr + is not set, the bias is initialized zero. Default: None. + name (str|None): A name for this layer(optional). If set None, the layer + will be named automatically. Default: None + Returns: + Variable: The tensor variable storing the deformable convolution \ + result. + Raises: + ValueError: If the shapes of input, filter_size, stride, padding and + groups mismatch. + Examples: + .. code-block:: python + + data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32') + offset = fluid.layers.data(name='offset', shape=[18, 32, 32], dtype='float32') + mask = fluid.layers.data(name='mask', shape=[9, 32, 32], dtype='float32') + out = fluid.layers.deformable_conv(input=data, offset=offset, mask=mask, + num_filters=2, filter_size=3, padding=1) + """ + + num_channels = input.shape[1] + assert param_attr is not False, "param_attr should not be False here." + + helper = LayerHelper('deformable_conv', **locals()) + dtype = helper.input_dtype() + + if not isinstance(input, Variable): + raise TypeError("Input of deformable_conv must be Variable") + if not isinstance(offset, Variable): + raise TypeError("Input Offset of deformable_conv must be Variable") + if not isinstance(mask, Variable): + raise TypeError("Input Mask of deformable_conv must be Variable") + + if groups is None: + num_filter_channels = num_channels + else: + if num_channels % groups != 0: + raise ValueError("num_channels must be divisible by groups.") + num_filter_channels = num_channels // groups + + filter_size = utils.convert_to_list(filter_size, 2, 'filter_size') + stride = utils.convert_to_list(stride, 2, 'stride') + padding = utils.convert_to_list(padding, 2, 'padding') + dilation = utils.convert_to_list(dilation, 2, 'dilation') + + input_shape = input.shape + filter_shape = [num_filters, int(num_filter_channels)] + filter_size + + def _get_default_param_initializer(): + filter_elem_num = filter_size[0] * filter_size[1] * num_channels + std = (2.0 / filter_elem_num)**0.5 + return Normal(0.0, std, 0) + + filter_param = helper.create_parameter( + attr=helper.param_attr, + shape=filter_shape, + dtype=dtype, + default_initializer=_get_default_param_initializer()) + + pre_bias = helper.create_variable_for_type_inference(dtype) + + helper.append_op( + type='deformable_conv', + inputs={ + 'Input': input, + 'Filter': filter_param, + 'Offset': offset, + 'Mask': mask, + }, + outputs={"Output": pre_bias}, + attrs={ + 'strides': stride, + 'paddings': padding, + 'dilations': dilation, + 'groups': groups, + 'deformable_groups': deformable_groups, + 'im2col_step': im2col_step, + }) + + output = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2) + return output + + +def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None): + """ + + This function returns a col buffer of sliding local blocks of input x, also known + as im2col for batched 2D image tensors. For each block under the convolution filter, + all element will be rearranged as a column. While the convolution filter silding over + the input feature map, a series of such columns will be formed. + + For each input :math:`X` with shape [N, C, H, W], the output shape [N, Cout, Lout] + can be calculated as following. + + .. math:: + + dkernel[0] &= dilations[0] \\times (kernel\_sizes[0] - 1) + 1 + + dkernel[1] &= dilations[1] \\times (kernel\_sizes[1] - 1) + 1 + + hout &= \\frac{H + paddings[0] + paddings[2] - dkernel[0]}{strides[0]} + 1 + + wout &= \\frac{W + paddings[1] + paddings[3] - dkernel[1]}{strides[1]} + 1 + + Cout &= C \\times kernel\_sizes[0] \\times kernel\_sizes[1] + + Lout &= hout \\times wout + + + Args: + x(Varaible): The input tensor of format [N, C, H, W]. + kernel_sizes(int|list): The size of convolution kernel, should be [k_h, k_w] + or an integer k treated as [k, k]. + strides(int|list): The strides, should be [stride_h, stride_w] + or an integer stride treated as [sride, stride]. + For default, strides will be [1, 1]. + paddings(int|list): The paddings of each dimension, should be + [padding_top, padding_left, padding_bottom, padding_right] + or [padding_h, padding_w] or an integer padding. + If [padding_h, padding_w] was given, it will expanded to + [padding_h, padding_w, padding_h, padding_w]. If an integer + padding was given, [padding, padding, padding, padding] will + be used. For default, paddings will be [0, 0, 0, 0] + dilations(int|list): the dilations of convolution kernel, shold be + [dilation_h, dilation_w], or an integer dialtion treated as + [dilation, dilation]. For default, it will be [1, 1]. + + + Returns: + Variable: The tensor variable corresponding to the sliding local blocks. The output shape is [N, Cout, Lout] as decribled above. Cout is the total number of values within each block, and Lout is the total number of such blocks. + + Examples: + + .. code-block:: python + + import paddle.fluid as fluid + x = fluid.layers.data(name = 'data', shape = [3, 224, 224], dtype = 'float32') + y = fluid.layers.unfold(x, [3, 3], 1, 1, 1) + """ + + helper = LayerHelper("unfold", **locals()) + + assert len(x.shape) == 4, \ + "input should be the format of [N, C, H, W]" + + if isinstance(kernel_sizes, int): + kernel_sizes = [kernel_sizes, kernel_sizes] + else: + assert isinstance(kernel_sizes, list) and (len(kernel_sizes) == 2), \ + "kernel_sizes should either be an integer or a list of two integers" + + if isinstance(strides, int): + strides = [strides, strides] + else: + assert isinstance(strides, list) and (len(strides) == 2), \ + "strides should either be an integer or a list of two integers" + + if isinstance(dilations, int): + dilations = [dilations, dilations] + else: + assert isinstance(dilations, list) and (len(dilations) == 2), \ + "dilations should either be an integer or a list of two integers" + + if isinstance(paddings, int): + paddings = [paddings] * 4 + elif isinstance(paddings, list): + if len(paddings) == 2: + paddings = paddings * 2 + elif len(paddings) == 4: + pass + else: + raise ValueError( + "paddings should either be an integer or a list of 2 or 4 integers" + ) + else: + raise ValueError( + "Unexpected type of paddings, it should be either an integer or a list" + "of 2 or 4 integers") + + out = helper.create_variable_for_type_inference(dtype=x.dtype) + helper.append_op( + type="unfold", + inputs={"X": x}, + outputs={"Y": out}, + attrs={ + "kernel_sizes": kernel_sizes, + "strides": strides, + "paddings": paddings, + "dilations": dilations + }) + return out + + +def deformable_roi_pooling(input, + rois, + trans, + no_trans=False, + spatial_scale=1.0, + group_size=[1, 1], + pooled_height=1, + pooled_width=1, + part_size=None, + sample_per_part=1, + trans_std=0.1, + position_sensitive=False, + name=None): + """ + Deformable PSROI Pooling Layer + + Args: + input (Variable):The input of Deformable PSROIPooling.The shape of input tensor is + [N,C,H,W]. Where N is batch size,C is number of input channels,H + is height of the feature, and W is the width of the feature. + rois (Variable): ROIs (Regions of Interest) to pool over.It should be + a 2-D LoDTensor of shape (num_rois, 4), the lod level + is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is + the top left coordinates, and (x2, y2) is the bottom + right coordinates. + trans (Variable): Offset of features on ROIs while pooling.The format is NCHW, where + N is number of ROIs, C is number of channels, which indicate the offset distance + in the x and y directions, H is pooled height, and W is pooled width. + no_trans (bool): Whether to add offset to get new value or not while roi pooling, which + value is True or False. Default: False. + spatial_scale (float): Ratio of input feature map height (or width) to raw image height (or width). + Equals the reciprocal of total stride in convolutional layers, Default: 1.0. + group_size (list|tuple): The number of groups which input channels are divided.(eg.number of input channels + is k1*k2*(C+1), which k1 and k2 are group width and height and C+1 is number of output + chanels. eg.(4, 6), which 4 is height of group and 6 is width of group. Default: [1, 1]. + pooled_height (integer): The pooled output height. Default: 1. + pooled_width (integer): The pooled output width. Default: 1. + part_size (list|tuple): The height and width of offset, eg.(4, 6), which height is 4 and width is 6, Default: + if None, default value is [pooled_height, pooled_width]. + sample_per_part (integer): The number of samples in each bin. Default: 1. + trans_std (float): Coefficient of offset. Default: 0.1. + position_sensitive (bool): Whether to choose deformable psroi pooling mode or not. Default: False. + name (str): Name of layer. Default: None. + Returns: + Variable: The tensor variable storing the deformable psroi pooling \ + result. + + + Examples: + .. code-block:: python + + input = fluid.layers.data(name="input", + shape=[2, 192, 64, 64], + dtype='float32', + append_batch_size=False) + rois = fluid.layers.data(name="rois", + shape=[4], + dtype='float32', + lod_level=1) + trans = fluid.layers.data(name="trans", + shape=[2, 384, 64, 64], + dtype='float32', + append_batch_size=False) + x = fluid.layers.nn.deformable_roi_pooling(input=input, + rois=rois, + trans=trans, + no_trans=False, + spatial_scale=1.0, + group_size=(1, 1), + pooled_height=8, + pooled_width=8, + part_size=(8, 8), + sample_per_part=4, + trans_std=0.1, + position_sensitive=False) + """ + + input_channels = input.shape[1] + if position_sensitive == False: + output_channels = input_channels + else: + output_channels = input_channels / pooled_height / pooled_width + + if part_size is None: + part_height = pooled_height + part_width = pooled_width + part_size = [part_height, part_width] + part_size = utils.convert_to_list(part_size, 2, 'part_size') + group_size = utils.convert_to_list(group_size, 2, 'group_size') + helper = LayerHelper('deformable_psroi_pooling', **locals()) + dtype = helper.input_dtype() + output = helper.create_variable_for_type_inference(dtype) + top_count = helper.create_variable_for_type_inference(dtype='int32') + helper.append_op( + type="deformable_psroi_pooling", + inputs={"Input": input, + "ROIs": rois, + "Trans": trans}, + outputs={"Output": output, + "TopCount": top_count}, + attrs={ + "no_trans": no_trans, + "spatial_scale": spatial_scale, + "output_dim": output_channels, + "group_size": group_size, + "pooled_height": pooled_height, + "pooled_width": pooled_width, + "part_size": part_size, + "sample_per_part": sample_per_part, + "trans_std": trans_std + }) + return output diff --git a/python/paddle/fluid/layers/ops.py b/python/paddle/fluid/layers/ops.py index 636e83996f005c016a2e13f8abbf292960cd9ab0..6c944da560d4c6b6599fe365b7c5fb8c2d9721f4 100644 --- a/python/paddle/fluid/layers/ops.py +++ b/python/paddle/fluid/layers/ops.py @@ -83,12 +83,13 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0): Examples: .. code-block:: python + import paddle.fluid as fluid result = fluid.layers.uniform_random(shape=[32, 784]) """ - locals_var = locals() if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) + locals_var = locals().copy() kwargs = dict() for name, val in locals_var.items(): if val is not None: @@ -102,7 +103,7 @@ _hard_shrink_ = generate_layer_fn('hard_shrink') def hard_shrink(x, threshold=None): - locals_var = locals() + locals_var = locals().copy() kwargs = dict() for name, val in locals_var.items(): if val is not None: @@ -123,7 +124,7 @@ _cum_sum_ = generate_layer_fn('cumsum') def cumsum(x, axis=None, exclusive=None, reverse=None): - locals_var = locals() + locals_var = locals().copy() kwargs = dict() for name, val in locals_var.items(): if val is not None: @@ -144,7 +145,7 @@ _thresholded_relu_ = generate_layer_fn('thresholded_relu') def thresholded_relu(x, threshold=None): - locals_var = locals() + locals_var = locals().copy() kwargs = dict() for name, val in locals_var.items(): if val is not None: diff --git a/python/paddle/fluid/layers/tensor.py b/python/paddle/fluid/layers/tensor.py index 9a0afcd4516ed20a5a723109256ab8b8ba204922..92d5e819e5dce105c09055c514de092e3c0254a5 100644 --- a/python/paddle/fluid/layers/tensor.py +++ b/python/paddle/fluid/layers/tensor.py @@ -83,9 +83,10 @@ def create_parameter(shape, the created parameter. Examples: - >>> W = fluid.layers.create_parameter(shape=[784, 200], dtype='float32') - >>> data = fluid.layers.data(name="img", shape=[64, 784], append_batch_size=False) - >>> hidden = fluid.layers.matmul(x=data, y=W) + .. code-block:: python + + import paddle.fluid.layers as layers + W = layers.create_parameter(shape=[784, 200], dtype='float32') """ helper = LayerHelper("create_parameter", **locals()) if attr is None: @@ -122,8 +123,9 @@ def create_global_var(shape, Examples: .. code-block:: python - var = fluid.create_global_var(shape=[2,3], value=1.0, dtype='float32', - persistable=True, force_cpu=True, name='new_var') + import paddle.fluid.layers as layers + var = layers.create_global_var(shape=[2,3], value=1.0, dtype='float32', + persistable=True, force_cpu=True, name='new_var') """ helper = LayerHelper("global_var", **locals()) var = helper.create_global_variable( @@ -244,7 +246,9 @@ def tensor_array_to_tensor(input, axis=1, name=None): Examples: .. code-block:: python - output, output_index = fluid.layers.tensor_array_to_tensor(input=tensor_array) + import paddle.fluid as fluid + tensor_array = fluid.layers.create_parameter(shape=[784, 200], dtype='float32') + output, output_index = fluid.layers.tensor_array_to_tensor(input=tensor_array) """ helper = LayerHelper('tensor_array_to_tensor', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) @@ -275,14 +279,23 @@ def sums(input, out=None): Examples: .. code-block:: python - tmp = fluid.layers.zeros(shape=[10], dtype='int32') - i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) - a0 = layers.array_read(array=tmp, i=i) - i = layers.increment(x=i) - a1 = layers.array_read(array=tmp, i=i) - mean_a0 = layers.mean(a0) - mean_a1 = layers.mean(a1) - a_sum = layers.sums(input=[mean_a0, mean_a1]) + import paddle.fluid as fluid + + # sum of several tensors + a0 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1) + a1 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=2) + a2 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=3) + sums = fluid.layers.sums(input=[a0, a1, a2]) + + # sum of a tensor array + array = fluid.layers.create_array('int64') + i = fluid.layers.zeros(shape=[1], dtype='int64', force_cpu=True) + fluid.layers.array_write(a0, array=array, i=i) + i = fluid.layers.increment(x=i) + fluid.layers.array_write(a1, array=array, i=i) + i = fluid.layers.increment(x=i) + fluid.layers.array_write(a2, array=array, i=i) + sums = fluid.layers.sums(input=array) """ helper = LayerHelper('sum', **locals()) if out is None: @@ -312,6 +325,8 @@ def assign(input, output=None): Examples: .. code-block:: python + import paddle.fluid as fluid + data = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32") out = fluid.layers.create_tensor(dtype='float32') hidden = fluid.layers.fc(input=data, size=10) fluid.layers.assign(hidden, out) @@ -372,6 +387,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None): Examples: .. code-block:: python + import paddle.fluid as fluid data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64') """ @@ -425,7 +441,9 @@ def fill_constant_batch_size_like(input, .. code-block:: python - data = fluid.layers.fill_constant_batch_size_like( + import paddle.fluid as fluid + like = fluid.layers.data(name='like', shape=[1], dtype='float32') + data = fluid.lgyers.fill_constant_batch_size_like( input=like, shape=[1], value=0, dtype='int64') """ @@ -583,6 +601,7 @@ def ones(shape, dtype, force_cpu=False): Examples: .. code-block:: python + import paddle.fluid as fluid data = fluid.layers.ones(shape=[1], dtype='int64') """ assert isinstance(shape, list) or isinstance( @@ -612,6 +631,7 @@ def zeros(shape, dtype, force_cpu=False): Examples: .. code-block:: python + import paddle.fluid as fluid data = fluid.layers.zeros(shape=[1], dtype='int64') """ return fill_constant(value=0.0, **locals()) @@ -635,9 +655,11 @@ def reverse(x, axis): Examples: .. code-block:: python - out = fluid.layers.reverse(x=in, axis=0) + import paddle.fluid as fluid + data = fluid.layers.data(name="data", shape=[4, 8], dtype="float32") + out = fluid.layers.reverse(x=data, axis=0) # or: - out = fluid.layers.reverse(x=in, axis=[0,1]) + out = fluid.layers.reverse(x=data, axis=[0,1]) """ if isinstance(axis, int): axis = [axis] @@ -732,6 +754,14 @@ def has_inf(x): Returns: Variable: The tensor variable storing the output, only a bool value. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + data = fluid.layers.data(name="input", shape=[4, 32, 32], dtype="float32") + res = fluid.layers.has_inf(data) + """ helper = LayerHelper("isinf", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) @@ -748,6 +778,14 @@ def has_nan(x): Returns: Variable: The tensor variable storing the output, only a bool value. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + data = fluid.layers.data(name="input", shape=[4, 32, 32], dtype="float32") + res = fluid.layers.has_nan(data) + """ helper = LayerHelper("isnan", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) @@ -765,6 +803,15 @@ def isfinite(x): Returns: Variable: The tensor variable storing the output, contains a bool value. + + Examples: + + .. code-block:: python + + var = fluid.layers.data(name="data", + shape=(4, 6), + dtype="float32") + out = fluid.layers.isfinite(v) """ helper = LayerHelper("isfinite", **locals()) out = helper.create_variable_for_type_inference(dtype=x.dtype) diff --git a/python/paddle/fluid/metrics.py b/python/paddle/fluid/metrics.py index c7c82f28e7c441b4aa24ffa81a8695e565d737d8..946c6ff6565745c8c686659f70d191f9757f4ee7 100644 --- a/python/paddle/fluid/metrics.py +++ b/python/paddle/fluid/metrics.py @@ -27,6 +27,7 @@ from .initializer import Constant from . import unique_name from .framework import Program, Variable, program_guard from . import layers +from .layers import detection __all__ = [ 'MetricBase', @@ -153,20 +154,25 @@ class CompositeMetric(MetricBase): Examples: .. code-block:: python - labels = fluid.layers.data(name="data", shape=[1], dtype="int32") - data = fluid.layers.data(name="data", shape=[32, 32], dtype="int32") - pred = fluid.layers.fc(input=data, size=1000, act="tanh") - comp = fluid.metrics.CompositeMetric() - acc = fluid.metrics.Precision() - recall = fluid.metrics.Recall() - comp.add_metric(acc) - comp.add_metric(recall) - for pass in range(PASSES): - comp.reset() - for data in train_reader(): - loss, preds, labels = exe.run(fetch_list=[cost, preds, labels]) + import numpy as np + preds = [[0.1], [0.7], [0.8], [0.9], [0.2], + [0.2], [0.3], [0.5], [0.8], [0.6]] + labels = [[0], [1], [1], [1], [1], + [0], [0], [0], [0], [0]] + preds = np.array(preds) + labels = np.array(labels) + + comp = fluid.metrics.CompositeMetric() + precision = fluid.metrics.Precision() + recall = fluid.metrics.Recall() + comp.add_metric(precision) + comp.add_metric(recall) + comp.update(preds=preds, labels=labels) - numpy_acc, numpy_recall = comp.eval() + numpy_precision, numpy_recall = comp.eval() + + print("expect precision: %.2f, got %.2f" % ( 3. / 5, numpy_precision ) ) + print("expect recall: %.2f, got %.2f" % (3. / 4, numpy_recall ) ) """ def __init__(self, name=None): @@ -215,20 +221,30 @@ class Precision(MetricBase): relevant instances among the retrieved instances. https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers - Note Precision is different with Accuracy in binary classifiers. - accuracy = true positive / total instances - precision = true positive / all positive instance + This class mangages the precision score for binary classification task. Examples: .. code-block:: python + import numpy as np + metric = fluid.metrics.Precision() - for pass in range(PASSES): - metric.reset() - for data in train_reader(): - loss, preds, labels = exe.run(fetch_list=[cost, preds, labels]) - metric.update(preds=preds, labels=labels) - numpy_precision = metric.eval() + + # generate the preds and labels + + preds = [[0.1], [0.7], [0.8], [0.9], [0.2], + [0.2], [0.3], [0.5], [0.8], [0.6]] + + labels = [[0], [1], [1], [1], [1], + [0], [0], [0], [0], [0]] + + preds = np.array(preds) + labels = np.array(labels) + + metric.update(preds=preds, labels=labels) + numpy_precision = metric.eval() + + print("expct precision: %.2f and got %.2f" % ( 3.0 / 5.0, numpy_precision)) """ def __init__(self, name=None): @@ -247,7 +263,7 @@ class Precision(MetricBase): for i in range(sample_num): pred = preds[i] label = labels[i] - if label == 1: + if pred == 1: if pred == label: self.tp += 1 else: @@ -266,16 +282,30 @@ class Recall(MetricBase): https://en.wikipedia.org/wiki/Precision_and_recall + This class mangages the recall score for binary classification task. + Examples: .. code-block:: python + import numpy as np + metric = fluid.metrics.Recall() - for pass in range(PASSES): - metric.reset() - for data in train_reader(): - loss, preds, labels = exe.run(fetch_list=[cost, preds, labels]) - metric.update(preds=preds, labels=labels) - numpy_recall = metric.eval() + + # generate the preds and labels + + preds = [[0.1], [0.7], [0.8], [0.9], [0.2], + [0.2], [0.3], [0.5], [0.8], [0.6]] + + labels = [[0], [1], [1], [1], [1], + [0], [0], [0], [0], [0]] + + preds = np.array(preds) + labels = np.array(labels) + + metric.update(preds=preds, labels=labels) + numpy_precision = metric.eval() + + print("expct precision: %.2f and got %.2f" % ( 3.0 / 4.0, numpy_precision)) """ def __init__(self, name=None): @@ -288,15 +318,16 @@ class Recall(MetricBase): raise ValueError("The 'preds' must be a numpy ndarray.") if not _is_numpy_(labels): raise ValueError("The 'labels' must be a numpy ndarray.") - sample_num = labels[0] + sample_num = labels.shape[0] + preds = np.rint(preds).astype("int32") + for i in range(sample_num): - pred = preds[i].astype("int32") + pred = preds[i] label = labels[i] if label == 1: if pred == label: self.tp += 1 - else: - if pred != label: + else: self.fn += 1 def eval(self): @@ -306,8 +337,7 @@ class Recall(MetricBase): class Accuracy(MetricBase): """ - Accumulate the accuracy from minibatches and compute the average accuracy - for every pass. + Calculate the mean accuracy over multiple batches. https://en.wikipedia.org/wiki/Accuracy_and_precision Args: @@ -316,18 +346,28 @@ class Accuracy(MetricBase): Examples: .. code-block:: python - labels = fluid.layers.data(name="data", shape=[1], dtype="int32") - data = fluid.layers.data(name="data", shape=[32, 32], dtype="int32") - pred = fluid.layers.fc(input=data, size=1000, act="tanh") - minibatch_accuracy = fluid.layers.accuracy(pred, label) - accuracy_evaluator = fluid.metrics.Accuracy() - for pass in range(PASSES): - accuracy_evaluator.reset() - for data in train_reader(): - batch_size = data[0] - loss = exe.run(fetch_list=[cost, minibatch_accuracy]) - accuracy_evaluator.update(value=minibatch_accuracy, weight=batch_size) - numpy_acc = accuracy_evaluator.eval() + #suppose we have batch_size = 128 + batch_size=128 + accuracy_manager = fluid.metrics.Accuracy() + + #suppose the accuracy is 0.9 for the 1st batch + batch1_acc = 0.9 + accuracy_manager.update(value = batch1_acc, weight = batch_size) + print("expect accuracy: %.2f, get accuracy: %.2f" % (batch1_acc, accuracy_manager.eval())) + + #suppose the accuracy is 0.8 for the 2nd batch + batch2_acc = 0.8 + + accuracy_manager.update(value = batch2_acc, weight = batch_size) + #the joint acc for batch1 and batch2 is (batch1_acc * batch_size + batch2_acc * batch_size) / batch_size / 2 + print("expect accuracy: %.2f, get accuracy: %.2f" % ((batch1_acc * batch_size + batch2_acc * batch_size) / batch_size / 2, accuracy_manager.eval())) + + #reset the accuracy_manager + accuracy_manager.reset() + #suppose the accuracy is 0.8 for the 3rd batch + batch3_acc = 0.8 + accuracy_manager.update(value = batch3_acc, weight = batch_size) + print("expect accuracy: %.2f, get accuracy: %.2f" % (batch3_acc, accuracy_manager.eval())) """ def __init__(self, name=None): @@ -348,10 +388,15 @@ class Accuracy(MetricBase): "The 'value' must be a number(int, float) or a numpy ndarray.") if not _is_number_(weight): raise ValueError("The 'weight' must be a number(int, float).") + if _is_number_(weight) and weight < 0: + raise ValueError("The 'weight' can not be negative") self.value += value * weight self.weight += weight def eval(self): + """ + Return the mean accuracy (float or numpy.array) for all accumulated batches. + """ if self.weight == 0: raise ValueError("There is no data in Accuracy Metrics. \ Please check layers.accuracy output has added to Accuracy.") @@ -371,17 +416,29 @@ class ChunkEvaluator(MetricBase): Examples: .. code-block:: python - labels = fluid.layers.data(name="data", shape=[1], dtype="int32") - data = fluid.layers.data(name="data", shape=[32, 32], dtype="int32") - pred = fluid.layers.fc(input=data, size=1000, act="tanh") - precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks = layers.chunk_eval( - input=pred, - label=label) + # init the chunck-level evaluation manager metric = fluid.metrics.ChunkEvaluator() - for data in train_reader(): - loss, preds, labels = exe.run(fetch_list=[cost, preds, labels]) - metric.update(num_infer_chunks, num_label_chunks, num_correct_chunks) - numpy_precision, numpy_recall, numpy_f1 = metric.eval() + + # suppose the model predict 10 chuncks, while 8 ones are correct and the ground truth has 9 chuncks. + num_infer_chunks = 10 + num_label_chunks = 9 + num_correct_chunks = 8 + + metric.update(num_infer_chunks, num_label_chunks, num_correct_chunks) + numpy_precision, numpy_recall, numpy_f1 = metric.eval() + + print("precision: %.2f, recall: %.2f, f1: %.2f" % (numpy_precision, numpy_recall, numpy_f1)) + + # the next batch, predicting 3 prefectly correct chuncks. + num_infer_chunks = 3 + num_label_chunks = 3 + num_correct_chunks = 3 + + metric.update(num_infer_chunks, num_label_chunks, num_correct_chunks) + numpy_precision, numpy_recall, numpy_f1 = metric.eval() + + print("precision: %.2f, recall: %.2f, f1: %.2f" % (numpy_precision, numpy_recall, numpy_f1)) + """ def __init__(self, name=None): @@ -430,12 +487,17 @@ class ChunkEvaluator(MetricBase): class EditDistance(MetricBase): """ Edit distance is a way of quantifying how dissimilar two strings - (e.g., words) are to one another by counting the minimum number - of operations required to transform one string into the other. + (e.g., words) are to each another by counting the minimum number + of edit operations (add, remove or replace) required to transform + one string into the other. Refer to https://en.wikipedia.org/wiki/Edit_distance - Accumulate edit distance sum and sequence number from mini-batches and - compute the average edit_distance and instance error of all batches. + This EditDistance class takes two inputs by using update function: + 1. distances: a (batch_size, 1) numpy.array, each element represents the + edit distance between two sequences. + 2. seq_num: a int|float value, standing for the number of sequence pairs. + + and returns the overall edit distance of multiple sequence-pairs. Args: name: the metrics name @@ -443,19 +505,37 @@ class EditDistance(MetricBase): Examples: .. code-block:: python - distances, seq_num = fluid.layers.edit_distance(input, label) - distance_evaluator = fluid.metrics.EditDistance() - for epoch in PASS_NUM: - distance_evaluator.reset() - for data in batches: - loss = exe.run(fetch_list=[cost] + list(edit_distance_metrics)) - distance_evaluator.update(distances, seq_num) - distance, instance_error = distance_evaluator.eval() + import numpy as np + + # suppose that batch_size is 128 + batch_size = 128 + + # init the edit distance manager + distance_evaluator = fluid.metrics.EditDistance("EditDistance") + + # generate the edit distance across 128 sequence pairs, the max distance is 10 here + edit_distances_batch0 = np.random.randint(low = 0, high = 10, size = (batch_size, 1)) + seq_num_batch0 = batch_size + + distance_evaluator.update(edit_distances_batch0, seq_num_batch0) + avg_distance, wrong_instance_ratio = distance_evaluator.eval() + print("the average edit distance for batch0 is %.2f and the wrong instance ratio is %.2f " % (avg_distance, wrong_instance_ratio)) - In the above example: + edit_distances_batch1 = np.random.randint(low = 0, high = 10, size = (batch_size, 1)) + seq_num_batch1 = batch_size - - 'distance' is the average of the edit distance in a pass. - - 'instance_error' is the instance error rate in a pass. + distance_evaluator.update(edit_distances_batch1, seq_num_batch1) + avg_distance, wrong_instance_ratio = distance_evaluator.eval() + print("the average edit distance for batch0 and batch1 is %.2f and the wrong instance ratio is %.2f " % (avg_distance, wrong_instance_ratio)) + + distance_evaluator.reset() + + edit_distances_batch2 = np.random.randint(low = 0, high = 10, size = (batch_size, 1)) + seq_num_batch2 = batch_size + + distance_evaluator.update(edit_distances_batch2, seq_num_batch2) + avg_distance, wrong_instance_ratio = distance_evaluator.eval() + print("the average edit distance for batch2 is %.2f and the wrong instance ratio is %.2f " % (avg_distance, wrong_instance_ratio)) """ @@ -466,6 +546,15 @@ class EditDistance(MetricBase): self.instance_error = 0 def update(self, distances, seq_num): + """ + Update the overall edit distance + + Args: + distances: a (batch_size, 1) numpy.array, each element represents the + edit distance between two sequences. + seq_num: a int|float value, standing for the number of sequence pairs. + + """ if not _is_numpy_(distances): raise ValueError("The 'distances' must be a numpy ndarray.") if not _is_number_(seq_num): @@ -477,6 +566,11 @@ class EditDistance(MetricBase): self.total_distance += total_distance def eval(self): + """ + Return two floats: + avg_distance: the average distance for all sequence pairs updated using the update function. + avg_instance_error: the ratio of sequence pairs whose edit distance is not zero. + """ if self.seq_num == 0: raise ValueError( "There is no data in EditDistance Metric. Please check layers.edit_distance output has been added to EditDistance." @@ -488,9 +582,9 @@ class EditDistance(MetricBase): class Auc(MetricBase): """ - Auc metric adapts to the binary classification. + The auc metric is for binary classification. Refer to https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve - Need to note that auc metric compute the value via Python natively. + Please notice that the auc metric is implemented with python, which may be a little bit slow. If you concern the speed, please use the fluid.layers.auc instead. The `auc` function creates four local variables, `true_positives`, @@ -511,12 +605,26 @@ class Auc(MetricBase): Examples: .. code-block:: python - pred = fluid.layers.fc(input=data, size=1000, act="tanh") - metric = fluid.metrics.Auc() - for data in train_reader(): - loss, preds, labels = exe.run(fetch_list=[cost, preds, labels]) - metric.update(preds, labels) - numpy_auc = metric.eval() + import numpy as np + # init the auc metric + auc_metric = fluid.metrics.Auc("ROC") + + # suppose that batch_size is 128 + batch_num = 100 + batch_size = 128 + + for batch_id in range(batch_num): + + class0_preds = np.random.random(size = (batch_size, 1)) + class1_preds = 1 - class0_preds + + preds = np.concatenate((class0_preds, class1_preds), axis=1) + + labels = np.random.randint(2, size = (batch_size, 1)) + auc_metric.update(preds = preds, labels = labels) + + # shall be some score closing to 0.5 as the preds are randomly assigned + print("auc for iteration %d is %.2f" % (batch_id, auc_metric.eval())) """ def __init__(self, name, curve='ROC', num_thresholds=4095): @@ -529,6 +637,15 @@ class Auc(MetricBase): self._stat_neg = [0] * _num_pred_buckets def update(self, preds, labels): + """ + Update the auc curve with the given predictions and labels + + Args: + preds: an numpy array in the shape of (batch_size, 2), preds[i][j] denotes the probability + of classifying the instance i into the class j. + labels: an numpy array in the shape of (batch_size, 1), labels[i] is either o or 1, representing + the label of the instance i. + """ if not _is_numpy_(labels): raise ValueError("The 'labels' must be a numpy ndarray.") if not _is_numpy_(preds): @@ -548,6 +665,9 @@ class Auc(MetricBase): return abs(x1 - x2) * (y1 + y2) / 2.0 def eval(self): + """ + Return the area (a float score) under auc curve + """ tot_pos = 0.0 tot_neg = 0.0 auc = 0.0 @@ -609,20 +729,38 @@ class DetectionMAP(object): Examples: .. code-block:: python - exe = fluid.Executor(place) - map_evaluator = fluid.Evaluator.DetectionMAP(input, - gt_label, gt_box, gt_difficult) - cur_map, accum_map = map_evaluator.get_map_var() - fetch = [cost, cur_map, accum_map] - for epoch in PASS_NUM: - map_evaluator.reset(exe) - for data in batches: - loss, cur_map_v, accum_map_v = exe.run(fetch_list=fetch) + import paddle.fluid.layers as layers - In the above example: + batch_size = -1 # can be any size + image_boxs_num = 10 + bounding_bboxes_num = 21 + + pb = layers.data(name='prior_box', shape=[image_boxs_num, 4], + append_batch_size=False, dtype='float32') + + pbv = layers.data(name='prior_box_var', shape=[image_boxs_num, 4], + append_batch_size=False, dtype='float32') + + loc = layers.data(name='target_box', shape=[batch_size, bounding_bboxes_num, 4], + append_batch_size=False, dtype='float32') + + scores = layers.data(name='scores', shape=[batch_size, bounding_bboxes_num, image_boxs_num], + append_batch_size=False, dtype='float32') + + nmsed_outs = fluid.layers.detection_output(scores=scores, + loc=loc, prior_box=pb, prior_box_var=pbv) + + gt_box = fluid.layers.data(name="gt_box", shape=[batch_size, 4], dtype="float32") + gt_label = fluid.layers.data(name="gt_label", shape=[batch_size, 1], dtype="float32") + difficult = fluid.layers.data(name="difficult", shape=[batch_size, 1], dtype="float32") + + exe = fluid.Executor(fluid.CUDAPlace(0)) + map_evaluator = fluid.metrics.DetectionMAP(nmsed_outs, gt_label, gt_box, difficult, class_num = 3) + + cur_map, accum_map = map_evaluator.get_map_var() - - 'cur_map_v' is the mAP of current mini-batch. - - 'accum_map_v' is the accumulative mAP of one pass. + # see detailed examples at + https://github.com/PaddlePaddle/models/blob/43cdafbb97e52e6d93cc5bbdc6e7486f27665fc8/PaddleCV/object_detection """ @@ -647,7 +785,7 @@ class DetectionMAP(object): label = layers.concat([gt_label, gt_box], axis=1) # calculate mean average precision (mAP) of current mini-batch - map = layers.detection_map( + map = detection.detection_map( input, label, class_num, @@ -672,7 +810,7 @@ class DetectionMAP(object): self.has_state = var # calculate accumulative mAP - accum_map = layers.detection_map( + accum_map = detection.detection_map( input, label, class_num, diff --git a/python/paddle/fluid/net_drawer.py b/python/paddle/fluid/net_drawer.py index 8485d7d32fed8554c6d9afd610db230f52497da1..f991310384f769ce091197b16db953e7af94a3c3 100644 --- a/python/paddle/fluid/net_drawer.py +++ b/python/paddle/fluid/net_drawer.py @@ -21,9 +21,9 @@ from collections import defaultdict import paddle.fluid.core as core import paddle.fluid.proto.framework_pb2 as framework_pb2 +from paddle.fluid.log_helper import get_logger -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) +logger = get_logger(__name__, logging.INFO) try: from .graphviz import Graph diff --git a/python/paddle/fluid/nets.py b/python/paddle/fluid/nets.py index 5e511ed2eb9ffaeada45046dbe6c2b7c15ae6d16..20fbd079f76de498a092a7b0ed8926e3f13d7bb1 100644 --- a/python/paddle/fluid/nets.py +++ b/python/paddle/fluid/nets.py @@ -390,6 +390,8 @@ def scaled_dot_product_attention(queries, Examples: .. code-block:: python + import paddle.fluid as fluid + queries = fluid.layers.data(name="queries", shape=[3, 5, 9], dtype="float32", @@ -516,7 +518,7 @@ def scaled_dot_product_attention(queries, key_dim_per_head = keys.shape[-1] // num_heads scaled_q = layers.scale(x=q, scale=key_dim_per_head**-0.5) - product = layers.matmul(x=k, y=scaled_q, transpose_y=True) + product = layers.matmul(x=scaled_q, y=k, transpose_y=True) weights = layers.reshape( x=layers.reshape( diff --git a/python/paddle/fluid/optimizer.py b/python/paddle/fluid/optimizer.py index 69bbef77f85bd25674235fbe0a54e9a70d43e714..006cd291439c9ce7ccda58896cf281d7febf7502 100644 --- a/python/paddle/fluid/optimizer.py +++ b/python/paddle/fluid/optimizer.py @@ -1,4 +1,4 @@ -# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -14,16 +14,16 @@ from __future__ import print_function +import numpy as np from collections import defaultdict -from .wrapped_decorator import signature_safe_contextmanager -from paddle.fluid.framework import Program, Variable, name_scope, default_main_program, default_startup_program from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table +from paddle.fluid.framework import Program, Variable, name_scope, default_main_program, default_startup_program from . import framework from . import layers from . import unique_name -from .backward import append_backward +from .backward import append_backward, _some_in_set_, _append_grad_suffix_ from .clip import append_gradient_clip_ops, error_clip_callback from .framework import program_guard from .initializer import Constant @@ -35,14 +35,15 @@ from .dygraph.learning_rate_scheduler import LearningRateDecay from paddle.fluid import core from paddle.fluid.layers import tensor from functools import reduce -import copy +from .wrapped_decorator import signature_safe_contextmanager __all__ = [ 'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl', 'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer', 'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'RMSPropOptimizer', 'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'LarsMomentum', - 'LarsMomentumOptimizer', 'DGCMomentumOptimizer' + 'LarsMomentumOptimizer', 'DGCMomentumOptimizer', 'LambOptimizer', + 'ExponentialMovingAverage', 'PipelineOptimizer' ] @@ -54,6 +55,7 @@ class Optimizer(object): but need to use one of it's implementation. """ + @imperative_base.no_grad def __init__(self, learning_rate, regularization=None, name=None): if framework.in_dygraph_mode(): if not isinstance(learning_rate, float) and \ @@ -61,14 +63,18 @@ class Optimizer(object): raise TypeError( "learning rate should be float or LearningRateDecay, got %s here" % type(learning_rate)) + if name is not None: + self._name = unique_name.generate(name) + else: + self._name = unique_name.generate(self.__class__.__name__) else: if not isinstance(learning_rate, float) and \ not isinstance(learning_rate, framework.Variable): raise TypeError( "learning rate should be float or Variable, got %s here" % type(learning_rate)) + self._name = name - self._name = name self.regularization = regularization self._learning_rate = learning_rate # the learning rate type should be inferenced from loss @@ -87,6 +93,90 @@ class Optimizer(object): self.helper = None self._opti_name_list = [] + def load(self, stat_dict): + """ + load optimizer with learning rate decay in dygraph mode + :return: None + + Args: + stat_dict: the dict load by load_persistable method + + Examples: + + .. code-block:: python + + from __future__ import print_function + import numpy as np + import paddle + import paddle.fluid as fluid + from paddle.fluid.optimizer import SGDOptimizer + from paddle.fluid.dygraph.nn import FC + from paddle.fluid.dygraph.base import to_variable + + class MLP(fluid.Layer): + def __init__(self, name_scope): + super(MLP, self).__init__(name_scope) + + self._fc1 = FC(self.full_name(), 10) + self._fc2 = FC(self.full_name(), 10) + + def forward(self, inputs): + y = self._fc1(inputs) + y = self._fc2(y) + return y + + with fluid.dygraph.guard(): + mlp = MLP('mlp') + optimizer2 = SGDOptimizer( + learning_rate=fluid.layers.natural_exp_decay( + learning_rate=0.1, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) + + train_reader = paddle.batch( + paddle.dataset.mnist.train(), batch_size=128, drop_last=True) + + for batch_id, data in enumerate(train_reader()): + dy_x_data = np.array( + [x[0].reshape(1, 28, 28) for x in data]).astype('float32') + + y_data = np.array([x[1] for x in data]).astype('int64').reshape( + 128, 1) + + img = to_variable(dy_x_data) + label = to_variable(y_data) + label._stop_gradient = True + cost = mlp(img) + avg_loss = fluid.layers.reduce_mean(cost) + avg_loss.backward() + optimizer.minimize(avg_loss) + mlp.clear_gradients() + fluid.dygraph.save_persistables( + mlp.state_dict(), [optimizer, optimizer2], "save_dir_2") + if batch_id == 2: + break + + with fluid.dygraph.guard(): + mlp_load = MLP('mlp') + optimizer_load2 = SGDOptimizer( + learning_rate=fluid.layers.natural_exp_decay( + learning_rate=0.1, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) + parameters, optimizers = fluid.dygraph.load_persistables( + "save_dir_2") + mlp_load.load_dict(parameters) + optimizer_load2.load(optimizers) + self.assertTrue(optimizer2._learning_rate.__dict__ == optimizer_load2._learning_rate.__dict__) + + """ + if framework.in_dygraph_mode(): + self._learning_rate = stat_dict[self._name] + else: + raise TypeError("load can only be used under DyGraph mode") + def get_opti_var_name_list(self): return self._opti_name_list @@ -462,6 +552,8 @@ class Optimizer(object): if framework.in_dygraph_mode(): with program_guard(framework.default_main_program(), framework.default_startup_program()): + params_grads = append_regularization_ops(params_grads, + self.regularization) optimize_ops = self._create_optimization_pass(params_grads) else: program = loss.block.program @@ -469,11 +561,13 @@ class Optimizer(object): optimize_ops = self.apply_gradients(params_grads) return optimize_ops + @imperative_base.no_grad def minimize(self, loss, startup_program=None, parameter_list=None, - no_grad_set=None): + no_grad_set=None, + grad_clip=None): """ Add operations to minimize `loss` by updating `parameter_list`. @@ -486,6 +580,7 @@ class Optimizer(object): in `parameter_list`. parameter_list (list): list of Variables to update. no_grad_set (set|None): set of Variables should be ignored. + grad_clip (GradClipBase|None) : Gradient clip strategy Returns: tuple: (optimize_ops, params_grads) which are, list of operators appended; @@ -496,9 +591,17 @@ class Optimizer(object): startup_program=startup_program, parameter_list=parameter_list, no_grad_set=no_grad_set) + + if grad_clip is not None and framework.in_dygraph_mode(): + # TODO(hongyu): FIX later, this is only for dygraph, should be work for static mode + params_grads = grad_clip(params_grads) + optimize_ops = self.apply_optimize( loss, startup_program=startup_program, params_grads=params_grads) + if framework.in_dygraph_mode(): + framework._dygraph_tracer()._clear_ops() + return optimize_ops, params_grads @@ -520,8 +623,31 @@ class SGDOptimizer(Optimizer): Examples: .. code-block:: python - sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.2) - sgd_optimizer.minimize(cost) + import paddle + import paddle.fluid as fluid + import numpy as np + + place = fluid.CPUPlace() + main = fluid.Program() + with fluid.program_guard(main): + x = fluid.layers.data(name='x', shape=[13], dtype='float32') + y = fluid.layers.data(name='y', shape=[1], dtype='float32') + y_predict = fluid.layers.fc(input=x, size=1, act=None) + cost = fluid.layers.square_error_cost(input=y_predict, label=y) + avg_cost = fluid.layers.mean(cost) + + sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) + sgd_optimizer.minimize(avg_cost) + + fetch_list = [avg_cost] + train_reader = paddle.batch( + paddle.dataset.uci_housing.train(), batch_size=1) + feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + for data in train_reader(): + exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) + """ def __init__(self, learning_rate, regularization=None, name=None): @@ -582,8 +708,31 @@ class MomentumOptimizer(Optimizer): Examples: .. code-block:: python - optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1) - optimizer.minimize(cost) + import paddle + import paddle.fluid as fluid + import numpy as np + + place = fluid.CPUPlace() + main = fluid.Program() + with fluid.program_guard(main): + x = fluid.layers.data(name='x', shape=[13], dtype='float32') + y = fluid.layers.data(name='y', shape=[1], dtype='float32') + y_predict = fluid.layers.fc(input=x, size=1, act=None) + cost = fluid.layers.square_error_cost(input=y_predict, label=y) + avg_cost = fluid.layers.mean(cost) + + moment_optimizer = fluid.optimizer.MomentumOptimizer(learning_rate=0.001, momentum=0.9) + moment_optimizer.minimize(avg_cost) + + fetch_list = [avg_cost] + train_reader = paddle.batch( + paddle.dataset.uci_housing.train(), batch_size=1) + feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + for data in train_reader(): + exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) + """ _velocity_acc_str = "velocity" @@ -836,7 +985,8 @@ class DGCMomentumOptimizer(MomentumOptimizer): helper = LayerHelper("dgc_clip_by_norm_op", **args) if name is None: - name = unique_name.generate(".".join([helper.name, 'tmp'])) + name = unique_name.generate_with_ignorable_key(".".join( + [helper.name, 'tmp'])) out = helper.create_variable( type=x.type, name=name, dtype=x.dtype, persistable=False) @@ -1006,8 +1156,22 @@ class AdagradOptimizer(Optimizer): Examples: .. code-block:: python + import paddle.fluid as fluid + import numpy as np + + np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32) + inp = fluid.layers.data( + name="inp", shape=[2, 2], append_batch_size=False) + out = fluid.layers.fc(inp, size=3) + out = fluid.layers.reduce_sum(out) optimizer = fluid.optimizer.Adagrad(learning_rate=0.2) - optimizer.minimize(cost) + optimizer.minimize(out) + + exe = fluid.Executor(fluid.CPUPlace()) + exe.run(fluid.default_startup_program()) + exe.run( + feed={"inp": np_inp}, + fetch_list=[out.name]) """ _moment_acc_str = "moment" @@ -1106,8 +1270,29 @@ class AdamOptimizer(Optimizer): Examples: .. code-block:: python - optimizer = fluid.optimizer.Adam(learning_rate=0.2) - optimizer.minimize(cost) + import paddle + import paddle.fluid as fluid + + place = fluid.CPUPlace() + main = fluid.Program() + with fluid.program_guard(main): + x = fluid.layers.data(name='x', shape=[13], dtype='float32') + y = fluid.layers.data(name='y', shape=[1], dtype='float32') + y_predict = fluid.layers.fc(input=x, size=1, act=None) + cost = fluid.layers.square_error_cost(input=y_predict, label=y) + avg_cost = fluid.layers.mean(cost) + + adam_optimizer = fluid.optimizer.AdamOptimizer(0.01) + adam_optimizer.minimize(avg_cost) + + fetch_list = [avg_cost] + train_reader = paddle.batch( + paddle.dataset.uci_housing.train(), batch_size=1) + feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + for data in train_reader(): + exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) """ _moment1_acc_str = "moment1" @@ -1251,6 +1436,33 @@ class AdamaxOptimizer(Optimizer): However, it is added here for numerical stability to prevent the division by 0 error. + Examples: + .. code-block:: python + + import paddle.fluid as fluid + import numpy + + # First create the Executor. + place = fluid.CPUPlace() # fluid.CUDAPlace(0) + exe = fluid.Executor(place) + + train_program = fluid.Program() + startup_program = fluid.Program() + with fluid.program_guard(train_program, startup_program): + data = fluid.layers.data(name='X', shape=[1], dtype='float32') + hidden = fluid.layers.fc(input=data, size=10) + loss = fluid.layers.mean(hidden) + adam = fluid.optimizer.Adamax(learning_rate=0.2) + adam.minimize(loss) + + # Run the startup program once and only once. + exe.run(startup_program) + + x = numpy.random.random(size=(10, 1)).astype('float32') + outs = exe.run(program=train_program, + feed={'X': x}, + fetch_list=[loss.name]) + Args: learning_rate (float|Variable): the learning rate used to update parameters. \ Can be a float value or a Variable with one float value as data element. @@ -1261,12 +1473,6 @@ class AdamaxOptimizer(Optimizer): fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. - Examples: - .. code-block:: python - - optimizer = fluid.optimizer.Adamax(learning_rate=0.2) - optimizer.minimize(cost) - Notes: Currently, AdamaxOptimizer doesn't support sparse parameter optimization. """ @@ -1389,6 +1595,13 @@ class DecayedAdagradOptimizer(Optimizer): Examples: .. code-block:: python + import paddle.fluid as fluid + import paddle.fluid.layers as layers + from paddle.fluid.optimizer import DecayedAdagrad + + x = layers.data( name='x', shape=[-1, 10], dtype='float32' ) + trans = layers.fc( x, 100 ) + cost = layers.reduce_mean( trans ) optimizer = fluid.optimizer.DecayedAdagrad(learning_rate=0.2) optimizer.minimize(cost) @@ -1610,8 +1823,31 @@ class RMSPropOptimizer(Optimizer): Examples: .. code-block:: python - optimizer = fluid.optimizer.RMSProp(0.0001) - _, params_grads = optimizer.minimize(cost) + import paddle + import paddle.fluid as fluid + import numpy as np + + place = fluid.CPUPlace() + main = fluid.Program() + with fluid.program_guard(main): + x = fluid.layers.data(name='x', shape=[13], dtype='float32') + y = fluid.layers.data(name='y', shape=[1], dtype='float32') + y_predict = fluid.layers.fc(input=x, size=1, act=None) + cost = fluid.layers.square_error_cost(input=y_predict, label=y) + avg_cost = fluid.layers.mean(cost) + + rms_optimizer = fluid.optimizer.RMSProp(learning_rate=0.1) + rms_optimizer.minimize(avg_cost) + + fetch_list = [avg_cost] + train_reader = paddle.batch( + paddle.dataset.uci_housing.train(), batch_size=1) + feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + for data in train_reader(): + exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) + """ _momentum_acc_str = "momentum" @@ -1746,8 +1982,30 @@ class FtrlOptimizer(Optimizer): Examples: .. code-block:: python - optimizer = fluid.optimizer.Ftrl(0.0001) - _, params_grads = optimizer.minimize(cost) + import paddle + import paddle.fluid as fluid + import numpy as np + + place = fluid.CPUPlace() + main = fluid.Program() + with fluid.program_guard(main): + x = fluid.layers.data(name='x', shape=[13], dtype='float32') + y = fluid.layers.data(name='y', shape=[1], dtype='float32') + y_predict = fluid.layers.fc(input=x, size=1, act=None) + cost = fluid.layers.square_error_cost(input=y_predict, label=y) + avg_cost = fluid.layers.mean(cost) + + ftrl_optimizer = fluid.optimizer.Ftrl(learning_rate=0.1) + ftrl_optimizer.minimize(avg_cost) + + fetch_list = [avg_cost] + train_reader = paddle.batch( + paddle.dataset.uci_housing.train(), batch_size=1) + feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + for data in train_reader(): + exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) Notes: Currently, FtrlOptimizer doesn't support sparse parameter optimization. @@ -1813,6 +2071,133 @@ class FtrlOptimizer(Optimizer): return ftrl_op +class LambOptimizer(AdamOptimizer): + """ + LAMB (Layer-wise Adaptive Moments optimizer for Batching training) Optimizer. + + LAMB Optimizer is designed to scale up the batch size of training without losing + accuracy, which supports adaptive element-wise updating and accurate layer-wise + correction. For more information, please refer to `Reducing BERT Pre-Training + Time from 3 Days to 76 Minutes `_ . + + The updating of parameters follows: + + .. math:: + + m_t^l & = \\beta_1 m_{t - 1}^l + (1 - \\beta_1)g_t^l + + v_t^l & = \\beta_2 v_{t - 1}^l + (1 - \\beta_2)g_t^l \odot g_t^l + + \\widehat{m}_t^l & = m_t^l/(1 - \\beta_1^t) + + \\widehat{v}_t^l & = v_t^l/(1 - \\beta_2^t) + + r_1 & = \\left \| w_{t-1}^l \\right \|_2 + + r_2 & = \\left \| \\frac{\\widehat{m}_t^l}{\\sqrt{\\widehat{v}_t^l+\\epsilon}} + \\lambda w_{t-1}^l \\right \|_2 + + r & = r_1 / r_2 + + \\eta^l & = r \\times \\eta + + w_t^l & = w_{t-1}^l -\\eta ^l \\times (\\frac{\\widehat{m}_t^l}{\\sqrt{\\widehat{v}_t^l+\\epsilon}} + \\lambda w_{t-1}^l) + + + where :math:`m` is the 1st moment, and :math:`v` the 2nd moment, :math:`\\eta` the + learning rate, :math:`\\lambda` the LAMB weight decay rate. + + Args: + learning_rate (float|Variable): the learning rate used to update parameters. \ + Can be a float value or a Variable with one \ + float value as data element. + lamb_weight_decay (float): The LAMB weight decay rate. + beta1 (float): The exponential decay rate for the 1st moment estimates. + beta2 (float): The exponential decay rate for the 2nd moment estimates. + epsilon (float): A small float value for numerical stability. + regularization: A Regularizer, such as + fluid.regularizer.L1DecayRegularizer. + name (str|None): An optional name prefix. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + data = fluid.layers.data(name='x', shape=[5], dtype='float32') + hidden = fluid.layers.fc(input=data, size=10) + cost = fluid.layers.mean(hidden) + + optimizer = fluid.optimizer.Lamb(learning_rate=0.002) + optimizer.minimize(cost) + """ + _moment1_acc_str = "moment1" + _moment2_acc_str = "moment2" + _beta1_pow_acc_str = "beta1_pow_acc" + _beta2_pow_acc_str = "beta2_pow_acc" + + def __init__(self, + learning_rate=0.001, + lamb_weight_decay=0.01, + beta1=0.9, + beta2=0.999, + epsilon=1e-6, + regularization=None, + name=None): + assert learning_rate is not None + assert lamb_weight_decay is not None + assert beta1 is not None + assert beta2 is not None + assert epsilon is not None + super(LambOptimizer, self).__init__( + learning_rate=learning_rate, + regularization=regularization, + beta1=beta1, + beta2=beta2, + epsilon=epsilon, + name=name) + self.type = "lamb" + self._weight_decay = lamb_weight_decay + + def _append_optimize_op(self, block, param_and_grad): + assert isinstance(block, framework.Block) + + moment1 = self._get_accumulator(self._moment1_acc_str, + param_and_grad[0]) + moment2 = self._get_accumulator(self._moment2_acc_str, + param_and_grad[0]) + beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str, + param_and_grad[0]) + beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str, + param_and_grad[0]) + + # create the lamb optimize op + lamb_op = block.append_op( + type=self.type, + inputs={ + "Param": param_and_grad[0], + "Grad": param_and_grad[1], + "LearningRate": self._create_param_lr(param_and_grad), + "Moment1": moment1, + "Moment2": moment2, + "Beta1Pow": beta1_pow_acc, + "Beta2Pow": beta2_pow_acc + }, + outputs={ + "ParamOut": param_and_grad[0], + "Moment1Out": moment1, + "Moment2Out": moment2 + }, + attrs={ + "beta1": self._beta1, + "beta2": self._beta2, + "epsilon": self._epsilon, + "weight_decay": self._weight_decay + }, + stop_gradient=True) + + return lamb_op + + # We short the class name, since users will use the optimizer with the package # name. The sample code: # @@ -1831,13 +2216,14 @@ Adadelta = AdadeltaOptimizer RMSProp = RMSPropOptimizer Ftrl = FtrlOptimizer LarsMomentum = LarsMomentumOptimizer +Lamb = LambOptimizer class ModelAverage(Optimizer): - """Accumulate the average of parameters whtin sliding window. The average + """Accumulate the average of parameters within sliding window. The average result will be saved in temporary variables which can be applied to parameter variables of current model by calling 'apply()' method. And the - 'restore()' method is used to restored the parameter values of current model. + 'restore()' method is used to restore the parameter values of current model. The size of average window is determined by average_window_rate, min_average_window, max_average_window and current update times. @@ -1849,22 +2235,45 @@ class ModelAverage(Optimizer): regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer. name: A optional name prefix. + Examples: .. code-block:: python - optimizer = fluid.optimizer.Momentum() - optimizer.minimize(cost) - model_average = fluid.optimizer.ModelAverage(0.15, - min_average_window=10000, - max_average_window=20000) - for pass_id in range(args.pass_num): - for data in train_reader(): - exe.run(fluid.default_main_program()...) + import paddle.fluid as fluid + import numpy + + # First create the Executor. + place = fluid.CPUPlace() # fluid.CUDAPlace(0) + exe = fluid.Executor(place) + train_program = fluid.Program() + startup_program = fluid.Program() + with fluid.program_guard(train_program, startup_program): + # build net + data = fluid.layers.data(name='X', shape=[1], dtype='float32') + hidden = fluid.layers.fc(input=data, size=10) + loss = fluid.layers.mean(hidden) + optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1) + optimizer.minimize(loss) + + # build ModelAverage optimizer + model_average = fluid.optimizer.ModelAverage(0.15, + min_average_window=10000, + max_average_window=20000) + + exe.run(startup_program) + x = numpy.random.random(size=(10, 1)).astype('float32') + outs = exe.run(program=train_program, + feed={'X': x}, + fetch_list=[loss.name]) + + # apply ModelAverage with model_average.apply(exe): - for data in test_reader(): - exe.run(inference_program...) + x = numpy.random.random(size=(10, 1)).astype('float32') + exe.run(program=train_program, + feed={'X': x}, + fetch_list=[loss.name]) """ def __init__(self, @@ -1884,7 +2293,8 @@ class ModelAverage(Optimizer): ).all_parameters(): if param.do_model_average != False: grad = param.block.create_var( - name=unique_name.generate(".".join([param.name, 'tmp'])), + name=unique_name.generate_with_ignorable_key(".".join( + [param.name, 'tmp'])), dtype=param.dtype, persistable=False, stop_gradient=True) @@ -1978,6 +2388,10 @@ class ModelAverage(Optimizer): @signature_safe_contextmanager def apply(self, executor, need_restore=True): """Apply average values to parameters of current model. + + Args: + executor(fluid.Executor): current executor. + need_restore(bool): If you finally need to do restore, set it to True. Default is True. """ executor.run(self.apply_program) try: @@ -1988,5 +2402,435 @@ class ModelAverage(Optimizer): def restore(self, executor): """Restore parameter values of current model. + + Args: + executor(fluid.Executor): current executor. + """ + executor.run(self.restore_program) + + +class ExponentialMovingAverage(object): + """ + Compute the moving average of parameters with exponential decay. + Given a parameter :math:`\\theta`, its exponential moving average (EMA) + will be + + .. math:: + + \\text{EMA}_0 & = 0 + + \\text{EMA}_t & = \\text{decay} * \\text{EMA}_{t-1} + (1 - \\text{decay}) * \\theta_t + + The average results calculated by **update()** method will be saved in + temporary variables which are created and maintained by the object, and can + be applied to parameters of current model by calling **apply()** method. And + the **restore()** method is used to restore the parameters. + + **Bias correction**. All EMAs are initialized to :math:`0` and hence they will be + zero biased, which can be corrected by divided by a factor + :math:`(1 - \\text{decay}^t)` , i.e., the actual EMAs applied to parameters + when calling **apply()** method would be + + .. math:: + + \\widehat{\\text{EMA}}_t = \\frac{\\text{EMA}_t}{1 - \\text{decay}^t} + + **Decay rate scheduling**. A large decay rate very close to 1 would result + in that the averages move very slowly. And a better strategy is to set a + relative smaller decay rate in the very beginning. The argument **thres_steps** + allows users to pass a Variable to schedule the decay rate, in this case, + the actual decay rate becomes + + .. math:: + + \\min(\\text{decay}, \\frac{1 + \\text{thres_steps}}{10 + \\text{thres_steps}}) + + Usually **thres_steps** can be the global training steps. + + + Args: + decay (float): The exponential decay rate, usually close to 1, such as + 0.999, 0.9999, ... . + thres_steps (Variable|None): If not `None`, schedule the decay rate. + name (str|None): An optional name prefix. + + + Examples: + + .. code-block:: python + + import paddle.fluid as fluid + + data = fluid.layers.data(name='x', shape=[5], dtype='float32') + hidden = fluid.layers.fc(input=data, size=10) + cost = fluid.layers.mean(hidden) + + optimizer = fluid.optimizer.Adam(learning_rate=0.001) + optimizer.minimize(cost) + + global_steps = fluid.layers.learning_rate_scheduler._decay_step_counter() + ema = fluid.optimizer.ExponentialMovingAverage(0.999, thres_steps=global_steps) + ema.update() + + # pseudo code + for pass_id in range(args.pass_num): + for data in train_reader(): + exe.run(fluid.default_main_program()...) + + # usage 1 + with ema.apply(exe): + for data in test_reader(): + exe.run(inference_program...) + + # usage 2 + with ema.apply(exe, need_restore=False): + for data in test_reader(): + exe.run(inference_program...) + ... + ema.restore(exe) + """ + + def __init__(self, decay=0.999, thres_steps=None, name=None): + self._decay = decay + self._thres_steps = thres_steps + self._name = name if name is not None else '' + self._decay_var = self._get_ema_decay() + + self._params_tmps = [] + for param in default_main_program().global_block().all_parameters(): + if param.do_model_average != False: + tmp = param.block.create_var( + name=unique_name.generate(".".join( + [self._name + param.name, 'ema_tmp'])), + dtype=param.dtype, + persistable=False, + stop_gradient=True) + self._params_tmps.append((param, tmp)) + + self._ema_vars = {} + for param, tmp in self._params_tmps: + with param.block.program._optimized_guard( + [param, tmp]), name_scope('moving_average'): + self._ema_vars[param.name] = self._create_ema_vars(param) + + self.apply_program = Program() + block = self.apply_program.global_block() + with program_guard(main_program=self.apply_program): + decay_pow = self._get_decay_pow(block) + for param, tmp in self._params_tmps: + param = block._clone_variable(param) + tmp = block._clone_variable(tmp) + ema = block._clone_variable(self._ema_vars[param.name]) + layers.assign(input=param, output=tmp) + # bias correction + ema = ema / (1.0 - decay_pow) + layers.assign(input=ema, output=param) + + self.restore_program = Program() + block = self.restore_program.global_block() + with program_guard(main_program=self.restore_program): + for param, tmp in self._params_tmps: + tmp = block._clone_variable(tmp) + param = block._clone_variable(param) + layers.assign(input=tmp, output=param) + + def _get_ema_decay(self): + with default_main_program()._lr_schedule_guard(): + decay_var = layers.tensor.create_global_var( + shape=[1], + value=self._decay, + dtype='float32', + persistable=True, + name="scheduled_ema_decay_rate") + + if self._thres_steps is not None: + decay_t = (self._thres_steps + 1.0) / (self._thres_steps + 10.0) + with layers.control_flow.Switch() as switch: + with switch.case(decay_t < self._decay): + layers.tensor.assign(decay_t, decay_var) + with switch.default(): + layers.tensor.assign( + np.array( + [self._decay], dtype=np.float32), + decay_var) + return decay_var + + def _get_decay_pow(self, block): + global_steps = layers.learning_rate_scheduler._decay_step_counter() + decay_var = block._clone_variable(self._decay_var) + decay_pow_acc = layers.elementwise_pow(decay_var, global_steps + 1) + return decay_pow_acc + + def _create_ema_vars(self, param): + param_ema = layers.create_global_var( + name=unique_name.generate(self._name + param.name + '_ema'), + shape=param.shape, + value=0.0, + dtype=param.dtype, + persistable=True) + + return param_ema + + def update(self): + """ + Update Exponential Moving Average. Should only call this method in + train program. + """ + for param, tmp in self._params_tmps: + with param.block.program._optimized_guard( + [param, tmp]), name_scope('moving_average'): + param_ema = self._ema_vars[param.name] + ema_t = param_ema * self._decay_var + param * (1 - + self._decay_var) + layers.assign(input=ema_t, output=param_ema) + + @signature_safe_contextmanager + def apply(self, executor, need_restore=True): + """ + Apply moving average to parameters for evaluation. + + Args: + executor (Executor): The Executor to execute applying. + need_restore (bool): Whether to restore parameters after applying. + """ + executor.run(self.apply_program) + try: + yield + finally: + if need_restore: + self.restore(executor) + + def restore(self, executor): + """Restore parameters. + + Args: + executor (Executor): The Executor to execute restoring. """ executor.run(self.restore_program) + + +class PipelineOptimizer(object): + def __init__(self, + optimizer, + cut_list=None, + place_list=None, + concurrency_list=None, + queue_size=30, + sync_steps=1, + start_cpu_core_id=0): + # TODO: check properties + self._optimizer = optimizer + self._cut_list = cut_list + self._place_list = place_list + self._concurrency_list = concurrency_list + self._queue_size = queue_size + self._sync_steps = sync_steps + self._start_cpu_core_id = start_cpu_core_id + + def create_vars(self, block, main_program): + used_var_set = set() + for op_idx in range(block.desc.op_size()): + op_desc = block.desc.op(op_idx) + vars = op_desc.input_arg_names() + op_desc.output_arg_names() + for var in vars: + if var in used_var_set: + continue + used_var_set.add(var) + source_var = main_program.block(0).var(str(var)) + block._clone_variable(source_var, False) + + def extract_section_opt_ops(self, ops, cut_point_name): + """ + Extract opt ops in the given section + """ + output_names = set(cut_point_name) + relevant_op_flags = [True] * len(ops) + for i, op in reversed(list(enumerate(ops))): + if _some_in_set_(op.desc.output_arg_names(), output_names): + for name in op.desc.input_arg_names(): + output_names.add(name) + else: + relevant_op_flags[i] = False + + op_path = [ops[i] for i in range(len(ops)) if relevant_op_flags[i]] + return op_path + + def find_input_output(self, ops, name, is_forward=True): + """ + Find the inputs or outputs of a section + """ + all_set = set() + part_set = set() + for op in ops: + if is_forward: + part_set.update(op.desc.output_arg_names()) + else: + part_set.update(op.desc.input_arg_names()) + all_set.update(op.desc.output_arg_names()) + all_set.update(op.desc.input_arg_names()) + return all_set - part_set + + def find_persistable_vars(self, ops, whole_parameters): + """ + find the persistable input vars in current section + """ + res = set() + for op in ops: + vars = op.desc.input_arg_names() + for var in vars: + if var in whole_parameters: + res.add(var) + return res + + def _is_opt_role_op(self, op): + op_maker = core.op_proto_and_checker_maker + optimize_role = core.op_proto_and_checker_maker.OpRole.Optimize + if op_maker.kOpRoleAttrName() in op.attr_names and \ + int(op.all_attrs()[op_maker.kOpRoleAttrName()]) & int(optimize_role) != 0: + return True + return False + + def _is_lr_role_op(self, op): + op_maker = core.op_proto_and_checker_maker + optimize_role = core.op_proto_and_checker_maker.OpRole.LRSched + if op_maker.kOpRoleAttrName() in op.attr_names and \ + int(op.all_attrs()[op_maker.kOpRoleAttrName()]) == int(optimize_role): + return True + return False + + def extract_section_ops(self, ops, cut_point_name): + """ + Extract ops in the given section + """ + output_names = set(cut_point_name) + relevant_op_flags = [True] * len(ops) + for i, op in reversed(list(enumerate(ops))): + if not self._is_opt_role_op(op) and _some_in_set_( + op.desc.output_arg_names(), output_names): + for name in op.desc.input_arg_names(): + output_names.add(name) + elif op.desc.type() == "print" and op.desc.input_arg_names()[ + 0] in output_names: + continue + else: + relevant_op_flags[i] = False + + op_path = [ops[i] for i in range(len(ops)) if relevant_op_flags[i]] + return op_path + + def find_section_opt(self, ops, params): + res = self.extract_section_opt_ops(ops, params) + return res + + def split_program(self, main_program, cut_list): + programs = [] + block = main_program.block(0) + whole_parameters = [e.name for e in block.all_parameters()] + cut_var_names = [] + cut_len = len(cut_list) + sec_params = [] + for i, cut_vars in enumerate(cut_list[:-1]): + cut_var_names.append([cut_var.name for cut_var in cut_vars]) + for i, cut_vars in reversed(list(enumerate(cut_list[:-1]))): + cut_var_names.append( + [_append_grad_suffix_(cut_var.name) for cut_var in cut_vars]) + if i == 0: + cut_var_names[-1] += [var.name for var in cut_list[-1]] + ops = block.ops[:] + for i, cut_vars in enumerate(cut_var_names): + program = { + "program": Program(), + "input_set": set(), + "output_set": set() + } + cur_ops = self.extract_section_ops(ops, cut_vars) + if i == 0: + for op in ops: + if self._is_lr_role_op(op): + cur_ops.append(op) + #prevent inplace in/out + program["input_set"].update( + self.find_input_output( + cur_ops, [], is_forward=True)) + for e in cur_ops: + ops.remove(e) + + if i < cut_len: + sec_params.append( + self.find_persistable_vars(cur_ops, whole_parameters)) + if i >= cut_len - 1: + opt_ops = self.find_section_opt(ops, + sec_params[2 * cut_len - 2 - i]) + + for e in opt_ops: + ops.remove(e) + cur_ops += opt_ops + + op_descs = [op.desc for op in cur_ops] + for op_desc in op_descs: + ap_op = program["program"].block(0).desc.append_op() + ap_op.copy_from(op_desc) + program["input_set"].update( + self.find_input_output( + cur_ops, cut_vars, is_forward=True)) + program["input_set"].update(sec_params[min(i, 2 * cut_len - 2 - i)]) + program["output_set"].update( + self.find_input_output( + cur_ops, cut_vars, is_forward=False)) + programs.append(program) + program = { + "program": Program(), + "input_set": set(), + "output_set": set() + } + op_descs = [op.desc for op in ops] + for op_desc in op_descs: + ap_op = program["program"].block(0).desc.append_op() + ap_op.copy_from(op_desc) + program["input_set"].update( + [cut_var.name + "@GRAD" for cut_var in cut_list[0]]) + program["input_set"].update( + self.find_input_output( + ops, [], is_forward=True)) + program["input_set"].update(sec_params[0]) + programs.append(program) + inputs = set() + for program in reversed(list(programs)): + output_list = list(program["output_set"]) + for output in output_list: + if output not in inputs: + program["output_set"].remove(output) + inputs.update(program["input_set"]) + return programs + + def minimize(self, + loss, + startup_program=None, + parameter_list=None, + no_grad_set=None): + self._optimizer.minimize(loss, startup_program, parameter_list, + no_grad_set) + program = loss.block.program + program_list = self.split_program(program, self._cut_list) + for p in program_list: + self.create_vars(p["program"].block(0), program) + whole_parameters = [e.name for e in program.block(0).all_parameters()] + param_need_sync = [] + for i, section_p in enumerate(program_list): + if not isinstance(self._place_list[i], core.CUDAPlace): + continue + section_var = [e for e in section_p["program"].block(0).vars] + for p in section_var: + if p in whole_parameters: + param_need_sync.append(p) + program._pipeline_opt = { + "trainer": "PipelineTrainer", + "device_worker": "Section", + "section_program_list": program_list, + "place_list": self._place_list, + "concurrency_list": self._concurrency_list, + "queue_size": self._queue_size, + "start_cpu_core_id": self._start_cpu_core_id, + "sync_steps": self._sync_steps, + "param_need_sync": param_need_sync + } diff --git a/python/paddle/fluid/parallel_executor.py b/python/paddle/fluid/parallel_executor.py index a2c6537effafcc2134d05a3f972f88ea3ec985b5..d4a1041a4bf0566fc5e8e80e28804f1a50f86733 100644 --- a/python/paddle/fluid/parallel_executor.py +++ b/python/paddle/fluid/parallel_executor.py @@ -125,12 +125,6 @@ class ParallelExecutor(object): num_trainers=1, trainer_id=0, scope=None): - sys.stderr.write( - 'ParallelExecutor is deprecated. ' - 'Please use CompiledProgram and Executor. CompiledProgram ' - 'is a central place for optimization and Executor is the ' - 'unified executor. Example can be found in compiler.py.\n') - if build_strategy is None: build_strategy = BuildStrategy() @@ -330,6 +324,7 @@ class ParallelExecutor(object): loss = fluid.layers.mean(hidden) place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() + exe = fluid.Executor(place) exe.run(startup_program) parallel_exe = fluid.ParallelExecutor(use_cuda=use_cuda, diff --git a/python/paddle/fluid/param_attr.py b/python/paddle/fluid/param_attr.py index b7ce1c0e4f59af6be2dfaa7db79b49f72de65b16..1778f4b55e7f99eaa2866c8e5db4af0e11166a67 100644 --- a/python/paddle/fluid/param_attr.py +++ b/python/paddle/fluid/param_attr.py @@ -202,11 +202,12 @@ class WeightNormParamAttr(ParamAttr): Examples: .. code-block:: python - + + import paddle.fluid as fluid data = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32") fc = fluid.layers.fc(input=data, size=1000, - param_attr=WeightNormParamAttr( + param_attr=fluid.WeightNormParamAttr( dim=None, name='weight_norm_param')) diff --git a/python/paddle/fluid/reader.py b/python/paddle/fluid/reader.py index c2322ec763475ab7aa5780b77b767a6d7550fc39..f1aca6e5b3e89c0979dc4eff50af2d5fe68bb836 100644 --- a/python/paddle/fluid/reader.py +++ b/python/paddle/fluid/reader.py @@ -12,12 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. -from . import core +from . import core, dygraph import six +import warnings +import numpy as np import threading -from .framework import Program, Variable, program_guard, default_main_program, default_startup_program +import paddle +from .framework import Program, Variable, program_guard, default_main_program, default_startup_program, in_dygraph_mode from .executor import global_scope -from .data_feeder import DataFeeder, BatchedTensorProvider +from .data_feeder import DataFeeder, BatchedTensorProvider, ListTensorProvider from .layers.io import monkey_patch_reader_methods, _copy_reader_var_, double_buffer from .unique_name import UniqueNameGenerator @@ -48,12 +51,13 @@ class PyReader(object): Args: feed_list (list(Variable)|tuple(Variable)): feed variable list. - The variables should be created by :code:`fluid.layers.data()`. + The variables should be created by :code:`fluid.layers.data()`. + it can be None under iterable mode. capacity (int): capacity of the queue maintained in PyReader object. use_double_buffer (bool): whether to use double_buffer_reader to speed up data feeding. iterable (bool): whether the created reader object is iterable. - + return_list (bool): whether the return value presented as list. Returns: reader (Reader): the created reader object. @@ -124,7 +128,7 @@ class PyReader(object): return reader image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32') - reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=True) + reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=True, return_list=False) user_defined_reader = reader_creator_random_image(784, 784) reader.decorate_sample_list_generator( @@ -138,26 +142,79 @@ class PyReader(object): for data in reader(): executor.run(feed=data) + + 3. If return_list=True, the return values would be presented as list instead of dict`. + + .. code-block:: python + + import paddle + import paddle.fluid as fluid + import numpy as np + + EPOCH_NUM = 3 + ITER_NUM = 5 + BATCH_SIZE = 10 + + def reader_creator_random_image(height, width): + def reader(): + for i in range(ITER_NUM): + yield np.random.uniform(low=0, high=255, size=[height, width]), + return reader + + image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32') + reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=True, return_list=True) + + user_defined_reader = reader_creator_random_image(784, 784) + reader.decorate_sample_list_generator( + paddle.batch(user_defined_reader, batch_size=BATCH_SIZE), + fluid.core.CPUPlace()) + # definition of network is omitted + executor = fluid.Executor(fluid.core.CPUPlace()) + executor.run(fluid.default_main_program()) + + for _ in range(EPOCH_NUM): + for data in reader(): + executor.run(feed={"image": data[0]}) """ unique_name_generator = UniqueNameGenerator() def __init__(self, - feed_list, - capacity, + feed_list=None, + capacity=None, use_double_buffer=True, - iterable=False): + iterable=True, + return_list=False): self._tensor_reader = None self._thread = None - self._iterable = iterable + self._feed_list = feed_list + if not capacity: + raise ValueError("Please give value to capacity.") + # force to use iterable mode under dygraph mode + if in_dygraph_mode(): + if not iterable: + warnings.warn( + "Please NOTE: dygraph can support iterable mode only.") + self._iterable = True + if not return_list: + warnings.warn( + "Please NOTE: dygraph can support return as list only.") + self._return_list = True + else: + self._iterable = iterable + self._return_list = return_list + if not self._feed_list: + raise Exception("Feed list must be given under static mode.") self._use_double_buffer = use_double_buffer self._capacity = capacity - self._feed_list = feed_list if not self._iterable: self._init_non_iterable() def _init_iterable(self, places): - self._var_names = [v.name for v in self._feed_list] + if in_dygraph_mode(): + self._var_names = [] + else: + self._var_names = [v.name for v in self._feed_list] self._places = _convert_places(places) self._queue = core.init_lod_tensor_blocking_queue(core.Variable(), self._capacity) @@ -240,6 +297,7 @@ class PyReader(object): def __init__(self, reader): self._reader = reader._reader self._reset = reader._reset + self._return_list = reader._return_list def __iter__(self): return self @@ -248,12 +306,28 @@ class PyReader(object): return self.next() def next(self): - ret = self._reader.read_next() - if ret: - return ret + if not in_dygraph_mode(): + if self._return_list: + ret = self._reader.read_next_list() + ret = ret[0] if ret is not None and len( + ret) > 0 else None + else: + ret = self._reader.read_next() + if ret: + return ret + else: + self._reset() + raise StopIteration else: - self._reset() - raise StopIteration + ret = self._reader.read_next_list() + if ret and ret[0]: + return [ + dygraph.base.to_variable(np.array(v)) + for v in ret[0] + ] + else: + self._reset() + raise StopIteration self._start() return Iterator(self) @@ -293,8 +367,9 @@ class PyReader(object): break ''' - assert not self._iterable, "start() cannot be called when PyReader is iterable" - self._start() + if not in_dygraph_mode(): + assert not self._iterable, "start() cannot be called when PyReader is iterable" + self._start() def reset(self): ''' @@ -327,8 +402,9 @@ class PyReader(object): break ''' - assert not self._iterable, "reset() cannot be called when PyReader is iterable" - self._reset() + if not in_dygraph_mode(): + assert not self._iterable, "reset() cannot be called when PyReader is iterable" + self._reset() def _start(self): def __thread_main__(): @@ -415,27 +491,35 @@ class PyReader(object): ''' assert batch_size > 0, "batch_size must be larger than 0" - has_lod = False - for f in self._feed_list: - if f.lod_level != 0: - has_lod = True - break - - if has_lod: + if not in_dygraph_mode(): + has_lod = False + for f in self._feed_list: + if f.lod_level != 0: + has_lod = True + break + + if has_lod: + self.decorate_sample_list_generator( + paddle.batch( + sample_generator, + batch_size=batch_size, + drop_last=drop_last), + places=places) + else: + reader = BatchedTensorProvider( + feed_list=self._feed_list, + place=core.CPUPlace(), + batch_size=batch_size, + generator=sample_generator, + drop_last=drop_last) + self.decorate_batch_generator(reader, places=places) + else: self.decorate_sample_list_generator( paddle.batch( sample_generator, batch_size=batch_size, drop_last=drop_last), places=places) - else: - reader = BatchedTensorProvider( - feed_list=self._feed_list, - place=core.CPUPlace(), - batch_size=batch_size, - generator=sample_generator, - drop_last=drop_last) - self.decorate_batch_generator(reader, places=places) def decorate_sample_list_generator(self, reader, places=None): ''' @@ -488,14 +572,22 @@ class PyReader(object): ''' assert self._tensor_reader is None, \ "Cannot reset the data source of PyReader" - with program_guard(Program(), Program()): - feeder = DataFeeder( - feed_list=self._feed_list, place=core.CPUPlace()) - paddle_reader = feeder.decorate_reader(reader, multi_devices=False) - - def __tensor_reader_impl__(): - for slots in paddle_reader(): - yield [slots[var.name] for var in self._feed_list] + if not in_dygraph_mode(): + with program_guard(Program(), Program()): + feeder = DataFeeder( + feed_list=self._feed_list, place=core.CPUPlace()) + paddle_reader = feeder.decorate_reader( + reader, multi_devices=False) + + def __tensor_reader_impl__(): + for slots in paddle_reader(): + yield [slots[var.name] for var in self._feed_list] + else: + provider = ListTensorProvider(reader, places) + + def __tensor_reader_impl__(): + for slots in provider(): + yield slots[0] self.decorate_batch_generator(__tensor_reader_impl__, places) diff --git a/python/paddle/fluid/regularizer.py b/python/paddle/fluid/regularizer.py index d8aace9fdfa601413bb4d4b1b2a309ba6a8e4ece..822029a372b31fd86fb8b4568b2346aa98c003db 100644 --- a/python/paddle/fluid/regularizer.py +++ b/python/paddle/fluid/regularizer.py @@ -124,11 +124,21 @@ class L2DecayRegularizer(WeightDecayRegularizer): Examples: .. code-block:: python + import paddle.fluid as fluid + main_prog = fluid.Program() + startup_prog = fluid.Program() + with fluid.program_guard(main_prog, startup_prog): + data = fluid.layers.data(name='image', shape=[3, 28, 28], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') + hidden = fluid.layers.fc(input=data, size=128, act='relu') + prediction = fluid.layers.fc(input=hidden, size=10, act='softmax') + loss = fluid.layers.cross_entropy(input=prediction, label=label) + avg_loss = fluid.layers.mean(loss) optimizer = fluid.optimizer.Adagrad( learning_rate=1e-4, regularization=fluid.regularizer.L2DecayRegularizer( regularization_coeff=0.1)) - optimizer.minimize(avg_cost) + optimizer.minimize(avg_loss) """ def __init__(self, regularization_coeff=0.0): @@ -152,8 +162,11 @@ class L2DecayRegularizer(WeightDecayRegularizer): assert isinstance(param, framework.Parameter) assert isinstance(block, framework.Block) - decay = block.create_var( - dtype=param.dtype, shape=param.shape, lod_level=param.lod_level) + if framework.in_dygraph_mode(): + decay = block.create_var(dtype=param.dtype, shape=param.shape) + else: + decay = block.create_var( + dtype=param.dtype, shape=param.shape, lod_level=param.lod_level) # Append Op to calculate decay block.append_op( @@ -183,11 +196,21 @@ class L1DecayRegularizer(WeightDecayRegularizer): Examples: .. code-block:: python + import paddle.fluid as fluid + main_prog = fluid.Program() + startup_prog = fluid.Program() + with fluid.program_guard(main_prog, startup_prog): + data = fluid.layers.data(name='image', shape=[3, 28, 28], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') + hidden = fluid.layers.fc(input=data, size=128, act='relu') + prediction = fluid.layers.fc(input=hidden, size=10, act='softmax') + loss = fluid.layers.cross_entropy(input=prediction, label=label) + avg_loss = fluid.layers.mean(loss) optimizer = fluid.optimizer.Adagrad( learning_rate=1e-4, regularization=fluid.regularizer.L1DecayRegularizer( regularization_coeff=0.1)) - optimizer.minimize(avg_cost) + optimizer.minimize(avg_loss) """ def __init__(self, regularization_coeff=0.0): @@ -211,8 +234,11 @@ class L1DecayRegularizer(WeightDecayRegularizer): assert isinstance(param, framework.Parameter) assert isinstance(block, framework.Block) - decay = block.create_var( - dtype=param.dtype, shape=param.shape, lod_level=param.lod_level) + if framework.in_dygraph_mode(): + decay = block.create_var(dtype=param.dtype, shape=param.shape) + else: + decay = block.create_var( + dtype=param.dtype, shape=param.shape, lod_level=param.lod_level) # Append sign op block.append_op( diff --git a/python/paddle/fluid/tests/CMakeLists.txt b/python/paddle/fluid/tests/CMakeLists.txt index d24417bbacb503d9ea70e68e7e0edb59e7dddbde..2d81fd431716f9f1aef3d9b76c166807495cfb17 100644 --- a/python/paddle/fluid/tests/CMakeLists.txt +++ b/python/paddle/fluid/tests/CMakeLists.txt @@ -1,6 +1,10 @@ file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py") string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}") +if(NOT WITH_DISTRIBUTE) + list(REMOVE_ITEM TEST_OPS test_communicator) +endif(NOT WITH_DISTRIBUTE) + foreach(src ${TEST_OPS}) py_test(${src} SRCS ${src}.py) endforeach() diff --git a/python/paddle/fluid/tests/demo/pyreader.py b/python/paddle/fluid/tests/demo/pyreader.py index bbcef4c3ff23d955662be10b5f4b96a66da4c7d8..6995346ffa61ea65119930296be2fba5a10c5451 100644 --- a/python/paddle/fluid/tests/demo/pyreader.py +++ b/python/paddle/fluid/tests/demo/pyreader.py @@ -80,19 +80,21 @@ def main(): train_reader.start() try: while True: - print 'train_loss', numpy.array( - trainer.run(fetch_list=[loss.name])) + print( + 'train_loss', + numpy.array(trainer.run(fetch_list=[loss.name]))) except fluid.core.EOFException: - print 'End of epoch', epoch_id + print('End of epoch', epoch_id) train_reader.reset() test_reader.start() try: while True: - print 'test loss', numpy.array( - tester.run(fetch_list=[test_loss.name])) + print( + 'test loss', + numpy.array(tester.run(fetch_list=[test_loss.name]))) except fluid.core.EOFException: - print 'End of testing' + print('End of testing') test_reader.reset() diff --git a/python/paddle/fluid/tests/test_detection.py b/python/paddle/fluid/tests/test_detection.py index e1c4c2eca08d4652ecda8e2579d342818c803f4a..e72a430ff5776dbc35aeba34cd0e0ef998223a87 100644 --- a/python/paddle/fluid/tests/test_detection.py +++ b/python/paddle/fluid/tests/test_detection.py @@ -16,6 +16,7 @@ from __future__ import print_function import paddle.fluid as fluid import paddle.fluid.layers as layers +from paddle.fluid.layers import detection from paddle.fluid.framework import Program, program_guard import unittest @@ -349,7 +350,7 @@ class TestDetectionMAP(unittest.TestCase): append_batch_size=False, dtype='float32') - map_out = layers.detection_map(detect_res, label, 21) + map_out = detection.detection_map(detect_res, label, 21) self.assertIsNotNone(map_out) self.assertEqual(map_out.shape, (1, )) print(str(program)) @@ -522,6 +523,32 @@ class TestMulticlassNMS(unittest.TestCase): self.assertIsNotNone(output) +class TestCollectFpnPropsals(unittest.TestCase): + def test_collect_fpn_proposals(self): + program = Program() + with program_guard(program): + multi_bboxes = [] + multi_scores = [] + for i in range(4): + bboxes = layers.data( + name='rois' + str(i), + shape=[10, 4], + dtype='float32', + lod_level=1, + append_batch_size=False) + scores = layers.data( + name='scores' + str(i), + shape=[10, 1], + dtype='float32', + lod_level=1, + append_batch_size=False) + multi_bboxes.append(bboxes) + multi_scores.append(scores) + fpn_rois = layers.collect_fpn_proposals(multi_bboxes, multi_scores, + 2, 5, 10) + self.assertIsNotNone(fpn_rois) + + class TestDistributeFpnProposals(unittest.TestCase): def test_distribute_fpn_proposals(self): program = Program() diff --git a/python/paddle/fluid/tests/test_lod_tensor.py b/python/paddle/fluid/tests/test_lod_tensor.py index 722b5f07b04f9374db3f262f5134347fe753ba19..9bd343c103f15de728be9e2f6caa0d644f3cda0f 100644 --- a/python/paddle/fluid/tests/test_lod_tensor.py +++ b/python/paddle/fluid/tests/test_lod_tensor.py @@ -15,6 +15,7 @@ from __future__ import print_function import paddle.fluid as fluid +import paddle.fluid.core as core from paddle.fluid.lod_tensor import create_lod_tensor, create_random_int_lodtensor import numpy as np import unittest @@ -96,6 +97,23 @@ class TestLoDTensor(unittest.TestCase): recursive_seq_lens) self.assertEqual(tensor.shape(), [10, 1]) + def test_print_lodtensor(self): + shape = [1] + recursive_seq_lens = [[2, 3, 5]] + dict_size = 100 + low = 0 + high = dict_size - 1 + tensor = create_random_int_lodtensor(recursive_seq_lens, shape, + fluid.CPUPlace(), low, high) + print(tensor) + self.assertTrue(isinstance(str(tensor), str)) + + if core.is_compiled_with_cuda(): + gtensor = create_random_int_lodtensor(recursive_seq_lens, shape, + fluid.CUDAPlace(0), low, high) + print(gtensor) + self.assertTrue(isinstance(str(gtensor), str)) + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/CMakeLists.txt b/python/paddle/fluid/tests/unittests/CMakeLists.txt index aa4fc5ceb905034183ff235e503a70017cb27bce..15569b339df75f194f6b3c2b9f39de319cb2ec55 100644 --- a/python/paddle/fluid/tests/unittests/CMakeLists.txt +++ b/python/paddle/fluid/tests/unittests/CMakeLists.txt @@ -8,6 +8,8 @@ if(NOT WITH_DISTRIBUTE) list(REMOVE_ITEM TEST_OPS test_simple_dist_transpiler) list(REMOVE_ITEM TEST_OPS test_listen_and_serv_op) LIST(REMOVE_ITEM TEST_OPS test_dist_mnist) + LIST(REMOVE_ITEM TEST_OPS test_dist_mnist_nccl) + LIST(REMOVE_ITEM TEST_OPS test_dist_mnist_lars) LIST(REMOVE_ITEM TEST_OPS test_dist_word2vec) LIST(REMOVE_ITEM TEST_OPS test_dist_ctr) LIST(REMOVE_ITEM TEST_OPS test_dist_simnet_bow) @@ -15,8 +17,11 @@ if(NOT WITH_DISTRIBUTE) LIST(REMOVE_ITEM TEST_OPS test_dist_text_classification) LIST(REMOVE_ITEM TEST_OPS test_nce_remote_table_op) LIST(REMOVE_ITEM TEST_OPS test_hsigmoid_remote_table_op) + LIST(REMOVE_ITEM TEST_OPS test_dist_fleet_ctr) endif(NOT WITH_DISTRIBUTE) +LIST(REMOVE_ITEM TEST_OPS test_launch) + if (NOT ${WITH_GPU}) LIST(REMOVE_ITEM TEST_OPS test_conv2d_fusion_op) LIST(REMOVE_ITEM TEST_OPS test_parallel_dygraph_mnist) # TODO(Yancey1989): parallel dygraph support CPU device in future @@ -29,6 +34,7 @@ list(REMOVE_ITEM TEST_OPS test_modified_huber_loss_op) # FIXME(qijun) https://gi list(REMOVE_ITEM TEST_OPS test_lstm_unit_op) # # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5185 list(REMOVE_ITEM TEST_OPS test_cond_op) # FIXME(qijun): https://github.com/PaddlePaddle/Paddle/issues/5101#issuecomment-339814957 + list(REMOVE_ITEM TEST_OPS op_test) # op_test is a helper python file, not a test list(REMOVE_ITEM TEST_OPS decorator_helper) # decorator_helper is a helper python file, not a test if(APPLE) @@ -61,12 +67,35 @@ function(py_test_modules TARGET_NAME) COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_modules_ENVS} ${PYTHON_EXECUTABLE} ${PADDLE_SOURCE_DIR}/tools/test_runner.py ${py_test_modules_MODULES} WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}) - if (py_test_modules_SERIAL) - set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1) + if (py_test_modules_SERIAL) + set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1) endif() set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 350) endif() endfunction() + +function(bash_test_modules TARGET_NAME) + if(NOT WITH_TESTING) + return() + endif() + + set(options SERIAL) + set(oneValueArgs "") + set(multiValueArgs MODULES DEPS ENVS) + cmake_parse_arguments(bash_test_modules "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + + message(STATUS "CMAKE_CURRENT_BINARY_DIR:" ${CMAKE_CURRENT_BINARY_DIR}) + + add_test(NAME ${TARGET_NAME} + COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${PADDLE_BINARY_DIR}/python ${bash_test_modules_ENVS} + bash ${CMAKE_CURRENT_BINARY_DIR}/${bash_test_modules_MODULES} + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}) + if (bash_test_modules_SERIAL) + set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1) + endif() + set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600) +endfunction() + list(REMOVE_ITEM TEST_OPS test_warpctc_op) list(REMOVE_ITEM TEST_OPS test_dist_train) list(REMOVE_ITEM TEST_OPS test_dist_transpiler) @@ -80,10 +109,13 @@ list(REMOVE_ITEM TEST_OPS test_parallel_executor_transformer) list(REMOVE_ITEM TEST_OPS test_bilinear_interp_op) list(REMOVE_ITEM TEST_OPS test_nearest_interp_op) list(REMOVE_ITEM TEST_OPS test_imperative_resnet) +list(REMOVE_ITEM TEST_OPS test_imperative_resnet_sorted_gradient) +list(REMOVE_ITEM TEST_OPS test_imperative_mnist_sorted_gradient) list(REMOVE_ITEM TEST_OPS test_imperative_se_resnext) list(REMOVE_ITEM TEST_OPS test_imperative_mnist) list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer) list(REMOVE_ITEM TEST_OPS test_layers) +list(REMOVE_ITEM TEST_OPS test_imperative_ocr_attention_model) # Some ops need to check results when gc is enabled # Currently, only ops that register NoNeedBufferVarsInference need to do this test @@ -124,43 +156,52 @@ foreach(TEST_OP ${TEST_OPS}) py_test_modules(${TEST_OP} MODULES ${TEST_OP}) endforeach(TEST_OP) py_test_modules(test_adam_op_multi_thread MODULES test_adam_op ENVS FLAGS_inner_op_parallelism=4) -py_test_modules(test_warpctc_op MODULES test_warpctc_op SERIAL) -py_test_modules(test_bilinear_interp_op MODULES test_bilinear_interp_op ENVS ${GC_ENVS} SERIAL) -py_test_modules(test_nearest_interp_op MODULES test_nearest_interp_op ENVS ${GC_ENVS} SERIAL) +py_test_modules(test_warpctc_op MODULES test_warpctc_op) +py_test_modules(test_bilinear_interp_op MODULES test_bilinear_interp_op ENVS ${GC_ENVS}) +py_test_modules(test_nearest_interp_op MODULES test_nearest_interp_op ENVS ${GC_ENVS}) py_test_modules(test_imperative_resnet MODULES test_imperative_resnet ENVS FLAGS_cudnn_deterministic=1 SERIAL) +set_tests_properties(test_imperative_resnet PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE") +py_test_modules(test_imperative_resnet_sorted_gradient MODULES test_imperative_resnet_sorted_gradient ENVS + FLAGS_cudnn_deterministic=1 SERIAL) +set_tests_properties(test_imperative_resnet_sorted_gradient PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE") py_test_modules(test_imperative_mnist MODULES test_imperative_mnist ENVS - FLAGS_cudnn_deterministic=1 SERIAL) + FLAGS_cudnn_deterministic=1) +py_test_modules(test_imperative_mnist_sorted_gradient MODULES test_imperative_mnist_sorted_gradient ENVS + FLAGS_cudnn_deterministic=1) py_test_modules(test_imperative_se_resnext MODULES test_imperative_se_resnext ENVS FLAGS_cudnn_deterministic=1 SERIAL) +set_tests_properties(test_imperative_se_resnext PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE") if(WITH_DISTRIBUTE) - py_test_modules(test_dist_train MODULES test_dist_train SERIAL) + py_test_modules(test_dist_train MODULES test_dist_train) set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20) if(WITH_DGC) py_test_modules(test_dgc_op MODULES test_dgc_op) endif() if(NOT APPLE) set_tests_properties(test_dist_mnist PROPERTIES TIMEOUT 200) + set_tests_properties(test_dist_mnist_nccl PROPERTIES TIMEOUT 250) + set_tests_properties(test_dist_mnist_lars PROPERTIES TIMEOUT 200) set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 200) py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext) - py_test_modules(test_dist_se_resnext_nccl MODULES test_dist_se_resnext_nccl SERIAL) + py_test_modules(test_dist_se_resnext_nccl MODULES test_dist_se_resnext_nccl) + bash_test_modules(test_launch MODULES test_launch.sh) # FIXME(typhoonzero): add these tests back # py_test_modules(test_dist_transformer MODULES test_dist_transformer) # set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000) - set_tests_properties(test_dist_ctr test_dist_mnist test_dist_mnist_batch_merge test_dist_save_load test_dist_se_resnext test_dist_simnet_bow test_dist_text_classification test_dist_train test_dist_word2vec PROPERTIES RUN_SERIAL TRUE) endif(NOT APPLE) # py_test_modules(test_dist_transpiler MODULES test_dist_transpiler) endif() -py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL) -py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL) +py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf) +py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed) set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 450) set_tests_properties(test_parallel_executor_seresnext PROPERTIES TIMEOUT 740) -py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL) +py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer) py_test_modules(test_layers MODULES test_layers ENVS FLAGS_cudnn_deterministic=1) if(NOT WIN32) - py_test_modules(test_ir_memory_optimize_transformer MODULES test_ir_memory_optimize_transformer SERIAL) + py_test_modules(test_ir_memory_optimize_transformer MODULES test_ir_memory_optimize_transformer) endif() if(CMAKE_BUILD_TYPE STREQUAL "Debug") @@ -182,5 +223,5 @@ if(WITH_DISTRIBUTE) endif() set_tests_properties(test_recordio_reader test_parallel_executor_test_while_train test_parallel_executor_mnist - test_parallel_executor_seresnext test_parallel_executor_crf + test_parallel_executor_seresnext test_parallel_executor_crf test_sync_batch_norm_op PROPERTIES LABELS "RUN_TYPE=DIST") diff --git a/python/paddle/fluid/tests/unittests/dist_ctr_reader.py b/python/paddle/fluid/tests/unittests/dist_ctr_reader.py index 48a4768782c1b4aa8ff6cfdbda9c8e8eb717d08f..c030afdd4ff9be323ccbc19ebb5e119a8c9f040b 100644 --- a/python/paddle/fluid/tests/unittests/dist_ctr_reader.py +++ b/python/paddle/fluid/tests/unittests/dist_ctr_reader.py @@ -16,9 +16,9 @@ import logging import paddle import tarfile -logging.basicConfig() -logger = logging.getLogger("paddle") -logger.setLevel(logging.INFO) +from paddle.fluid.log_helper import get_logger + +logger = get_logger("paddle", logging.INFO) DATA_URL = "http://paddle-ctr-data.bj.bcebos.com/avazu_ctr_data.tgz" DATA_MD5 = "c11df99fbd14e53cd4bfa6567344b26e" diff --git a/python/paddle/fluid/tests/unittests/gradient_checker.py b/python/paddle/fluid/tests/unittests/gradient_checker.py index 87c917873cd97f7512621d45f64b2ae9e76bd33b..3775f62097d277e4ae4331070c74933233298a6e 100644 --- a/python/paddle/fluid/tests/unittests/gradient_checker.py +++ b/python/paddle/fluid/tests/unittests/gradient_checker.py @@ -23,7 +23,6 @@ from itertools import product import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.executor import Executor -from paddle.fluid.backward import calc_gradient from paddle.fluid.backward import _append_grad_suffix_, _as_list @@ -183,7 +182,7 @@ def _compute_analytical_jacobian(program, x, y, place, scope): dy = program.global_block().create_var( name=dy_name, shape=y.shape, dtype=np_type, persistable=True) # append backward - dx = calc_gradient(y, x, dy) + dx = fluid.gradients(y, x, dy) # init dy tensor in scope value = np.zeros(y.shape, dtype=np_type) @@ -196,17 +195,23 @@ def _compute_analytical_jacobian(program, x, y, place, scope): x = _as_list(x) jacobian = make_jacobian(x, y_size, np_type) + # filter None in dx for DX/DY may be None in kernel + # only fetch not None dx in exe.run + filted = [(i, dxi) for i, dxi in enumerate(dx) if dxi is not None] + filted_idx, filted_dx = zip(*filted) + for i in six.moves.xrange(y_size): _set_item(dy_t, i, 1, np_type) - dx_res = exe.run(program, scope=scope, fetch_list=dx) + dx_res = exe.run(program, scope=scope, fetch_list=filted_dx) - for j in six.moves.xrange(len(x)): + for j in six.moves.xrange(len(filted_dx)): + dx_idx = filted_idx[j] if dx_res[j] is not None: - jacobian[j][:, i] = dx_res[j].flatten() + jacobian[dx_idx][:, i] = dx_res[j].flatten() else: - jacobian[j][:, i] = np.zeros( - dx[j].shape, dtype=np_type).flatten() + jacobian[dx_idx][:, i] = np.zeros( + dx[dx_idx].shape, dtype=np_type).flatten() _set_item(dy_t, i, 0, np_type) @@ -376,7 +381,7 @@ def double_grad_check(x, ] # append first order grads - target_grads = calc_gradient(y, x, y_grads) + target_grads = fluid.gradients(y, x, y_grads) # y_grads are the input of first-order backward, # so, they are also the input of second-order backward. diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py index 28b670d7ab3267a03157b7e617504eb9a35656aa..6e4f0166121a6478399973d2c7a3aa7e1cb5506c 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py @@ -57,6 +57,8 @@ class TestConv2dMKLDNNOp(TestConv2dOp): self.fuse_bias = False self.bias_size = None self.fuse_relu = False + self.fuse_brelu = False + self.fuse_brelu_threshold = 6.0 self.fuse_residual_connection = False self.input_residual_size = None TestConv2dOp.setUp(self) @@ -84,15 +86,38 @@ class TestConv2dMKLDNNOp(TestConv2dOp): if self.fuse_relu: output = np.maximum(output, 0).astype(self.dsttype) + if self.fuse_brelu: + output = np.minimum( + np.maximum(output, 0), + self.fuse_brelu_threshold).astype(self.dsttype) output = output.astype(self.dtype) self.attrs['fuse_bias'] = self.fuse_bias self.attrs['fuse_relu'] = self.fuse_relu + self.attrs['fuse_brelu'] = self.fuse_brelu + self.attrs['fuse_brelu_threshold'] = self.fuse_brelu_threshold self.attrs['fuse_residual_connection'] = self.fuse_residual_connection self.outputs['Output'] = output +class TestWithbreluFusion(TestConv2dMKLDNNOp): + def init_test_case(self): + TestConv2dMKLDNNOp.init_test_case(self) + self.fuse_brelu = True + self.fuse_brelu_threshold = 6.0 + self.dsttype = np.float32 + + def test_check_grad(self): + pass + + def test_check_grad_no_filter(self): + pass + + def test_check_grad_no_input(self): + pass + + class TestWithFuse(TestConv2dMKLDNNOp): def init_test_case(self): TestConv2dMKLDNNOp.init_test_case(self) diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_fc_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_fc_mkldnn_op.py index 84229a5cffbb466ef3c69cd997adacfb21f6aae2..8f0a9898dce32b7162a710ed23bde4c1f7c7a1ff 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_fc_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_fc_mkldnn_op.py @@ -20,34 +20,30 @@ from paddle.fluid.tests.unittests.op_test import OpTest def fully_connected_naive(input, weights, bias_data=None): - in_n, in_c, in_h, in_w = input.shape - w_h, w_c = weights.shape - - x_data = np.reshape(input, [in_n, in_c * in_h * in_w]) - # this transpose should be implemented at C code - w_data = np.transpose(np.reshape(weights, (w_c, in_c * in_h * in_w))) result = None if not bias_data: - result = np.dot(x_data, w_data) + result = np.dot(input, weights) else: - result = np.dot(x_data, w_data) + bias_data + result = np.dot(input, weights) + bias_data return result class MatrixGenerate: def __init__(self, mb, ic, oc, h, w): - self.input = np.random.random((mb, ic, h, w)).astype("float32") + self.input = np.random.random((mb, ic * h * w)).astype("float32") self.weights = np.random.random((ic * h * w, oc)).astype("float32") class TestFCMKLDNNOp(OpTest): + def create_data(self): + self.matrix = MatrixGenerate(1, 10, 15, 3, 3) + def setUp(self): self.op_type = "fc" self.use_mkldnn = True - self.matrix = MatrixGenerate(1, 10, 15, 3, 3) - + self.create_data() self.inputs = {'Input': self.matrix.input, 'W': self.matrix.weights} self.attrs = {'use_mkldnn': self.use_mkldnn, } @@ -60,37 +56,16 @@ class TestFCMKLDNNOp(OpTest): self.check_output() def test_check_grad_normal(self): - self.check_grad(set(['Input', 'W']), 'Out', max_relative_error=0.9) + pass def test_check_grad_no_weight(self): - self.check_grad( - ['Input'], 'Out', max_relative_error=0.5, no_grad_set=set('W')) + pass class TestFCMKLDNNOp1(TestFCMKLDNNOp): - def init_op_type(self): + def create_data(self): self.matrix = MatrixGenerate(2, 15, 48, 2, 2) -class TestFCMKLDNNOp2(TestFCMKLDNNOp): - def init_op_type(self): - self.matrix = MatrixGenerate(2, 32, 40, 1, 1) - - -class TestFCMKLDNNOp3(TestFCMKLDNNOp): - def init_op_type(self): - self.matrix = MatrixGenerate(2, 2, 4, 1, 1) - - -class TestFCMKLDNNOp4(TestFCMKLDNNOp): - def init_op_type(self): - self.matrix = MatrixGenerate(2, 32, 48, 2, 2) - - -class TestFCMKLDNNOp4(TestFCMKLDNNOp): - def init_op_type(self): - self.matrix = MatrixGenerate(2, 32, 1000, 6, 6) - - if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_activation_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_activation_ngraph_op.py index 034d7792c13efb432e6bef6c95ee554584f29519..a7f167cbd415c9000311aa45bef0432f61e668ea 100644 --- a/python/paddle/fluid/tests/unittests/ngraph/test_activation_ngraph_op.py +++ b/python/paddle/fluid/tests/unittests/ngraph/test_activation_ngraph_op.py @@ -18,7 +18,7 @@ import unittest import numpy as np import paddle.fluid.core as core from paddle.fluid.tests.unittests.op_test import OpTest -from paddle.fluid.tests.unittests.test_activation_op import TestSigmoid, TestRelu, TestTanh +from paddle.fluid.tests.unittests.test_activation_op import TestAbs, TestGelu, TestSigmoid, TestSquare, TestRelu, TestTanh class TestNGRAPHReluDim4(TestRelu): diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_conv2d_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_conv2d_ngraph_op.py index ff2e865b66a5f1166281c267392b0964ca5b3082..764d136ec8d1e4f6772d4d1cdd03b6494aa735d1 100644 --- a/python/paddle/fluid/tests/unittests/ngraph/test_conv2d_ngraph_op.py +++ b/python/paddle/fluid/tests/unittests/ngraph/test_conv2d_ngraph_op.py @@ -15,7 +15,40 @@ from __future__ import print_function import unittest -from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride, TestWithGroup, TestWith1x1, TestWithInput1x1Filter1x1 +from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride, TestWithGroup, TestWith1x1, TestWithInput1x1Filter1x1, TestDepthwiseConv, TestDepthwiseConv2, TestDepthwiseConv3, TestDepthwiseConvWithDilation, TestDepthwiseConvWithDilation2 + + +class TestNGRAPHDepthwiseConv(TestDepthwiseConv): + def init_test_case(self): + super(TestNGRAPHDepthwiseConv, self).init_test_case() + self.use_cuda = False + + +class TestNGRAPHDepthwiseConv2(TestDepthwiseConv2): + def init_test_case(self): + super(TestNGRAPHDepthwiseConv2, self).init_test_case() + self.use_cuda = False + + +class TestNGRAPHDepthwiseConv3(TestDepthwiseConv3): + def init_test_case(self): + super(TestNGRAPHDepthwiseConv3, self).init_test_case() + self.use_cuda = False + + +class TestNGRAPHDepthwiseConvWithDilation(TestDepthwiseConvWithDilation): + def init_test_case(self): + super(TestNGRAPHDepthwiseConvWithDilation, self).init_test_case() + self.use_cuda = False + + +class TestNGRAPHDepthwiseConvWithDilation2(TestDepthwiseConvWithDilation2): + def init_test_case(self): + super(TestNGRAPHDepthwiseConvWithDilation2, self).init_test_case() + self.use_cuda = False + + +del TestDepthwiseConv, TestDepthwiseConv2, TestDepthwiseConv3, TestDepthwiseConvWithDilation, TestDepthwiseConvWithDilation2 if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py b/python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py index 8b9e2997ec702882b0e374cefd47b1c02343b225..3890236013c8a29288acde08198dd05abaeb6620 100644 --- a/python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py +++ b/python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py @@ -55,7 +55,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer): self._conv2d = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, @@ -101,11 +100,13 @@ class MNIST(fluid.dygraph.Layer): loc=0.0, scale=scale)), act="softmax") - def forward(self, inputs): + def forward(self, inputs, label): x = self._simple_img_conv_pool_1(inputs) x = self._simple_img_conv_pool_2(x) - x = self._fc(x) - return x + cost = self._fc(x) + loss = fluid.layers.cross_entropy(cost, label) + avg_loss = fluid.layers.mean(loss) + return avg_loss class TestMnist(TestParallelDyGraphRunnerBase): @@ -113,7 +114,7 @@ class TestMnist(TestParallelDyGraphRunnerBase): model = MNIST("mnist") train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=2, drop_last=True) - opt = SGDOptimizer(learning_rate=1e-3) + opt = fluid.optimizer.SGD(learning_rate=1e-3) return model, train_reader, opt def run_one_loop(self, model, opt, data): @@ -126,9 +127,8 @@ class TestMnist(TestParallelDyGraphRunnerBase): label = to_variable(y_data) label.stop_gradient = True - cost = model(img) - loss = fluid.layers.cross_entropy(cost, label) - avg_loss = fluid.layers.mean(loss) + avg_loss = model(img, label) + return avg_loss diff --git a/python/paddle/fluid/tests/unittests/test_async_ssa_graph_executor_mnist.py b/python/paddle/fluid/tests/unittests/test_async_ssa_graph_executor_mnist.py index 5e77ce9b811bc0474f1e0950e15dedf013dcb4ea..abc463a0fb0f8b521f0d833a1f9cd507718d3c9d 100644 --- a/python/paddle/fluid/tests/unittests/test_async_ssa_graph_executor_mnist.py +++ b/python/paddle/fluid/tests/unittests/test_async_ssa_graph_executor_mnist.py @@ -105,18 +105,23 @@ def train(use_cuda, thread_num, cpu_num): img, label, prediction, avg_loss, acc, py_reader = convolutional_neural_network( use_py_reader=True) + print("build convolutional neural network done.") optimizer = fluid.optimizer.Adam(learning_rate=0.001) optimizer.minimize(avg_loss) + print("Adam optimizer minimize done.") train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.mnist.train(), buf_size=500), batch_size=BATCH_SIZE) + print("declared train reader done.") place = fluid.CPUPlace() exe = fluid.Executor(place) + print("going to run startup program") exe.run(fluid.default_startup_program()) + print("run startup program done.") os.environ['CPU_NUM'] = str(cpu_num) @@ -137,6 +142,7 @@ def train(use_cuda, thread_num, cpu_num): main_program=main_program, build_strategy=build_strategy, exec_strategy=exec_strategy) + print("declare parallel executor done.") py_reader.decorate_paddle_reader(train_reader) diff --git a/python/paddle/fluid/tests/unittests/test_concat_op.py b/python/paddle/fluid/tests/unittests/test_concat_op.py index 42276a0647d95173d064bd1609ce743d7933ab79..b5d1115723e350f56e0d3e04d191886e43a15667 100644 --- a/python/paddle/fluid/tests/unittests/test_concat_op.py +++ b/python/paddle/fluid/tests/unittests/test_concat_op.py @@ -25,9 +25,15 @@ class TestConcatOp(OpTest): self.init_test_data() self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]} self.attrs = {'axis': self.axis} + if self.axis < 0: + self.actual_axis = self.axis + len(self.x0.shape) + self.actual_axis = self.actual_axis if self.actual_axis > 0 else 0 + else: + self.actual_axis = self.axis + self.outputs = { 'Out': np.concatenate( - (self.x0, self.x1, self.x2), axis=self.axis) + (self.x0, self.x1, self.x2), axis=self.actual_axis) } def test_check_output(self): @@ -75,5 +81,13 @@ class TestConcatOp4(TestConcatOp): pass +class TestConcatOp5(TestConcatOp): + def init_test_data(self): + self.x0 = np.random.random((2, 1, 4, 5)).astype('float32') + self.x1 = np.random.random((2, 2, 4, 5)).astype('float32') + self.x2 = np.random.random((2, 3, 4, 5)).astype('float32') + self.axis = -3 + + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_dist_base.py b/python/paddle/fluid/tests/unittests/test_dist_base.py index 6c7054e95efa7eefd574bc9025e23908dd4ac7b1..6daf9f8994d6f25989599587fe093d4b75452473 100644 --- a/python/paddle/fluid/tests/unittests/test_dist_base.py +++ b/python/paddle/fluid/tests/unittests/test_dist_base.py @@ -24,17 +24,26 @@ import six import argparse import pickle import numpy as np - +import time import paddle.fluid as fluid from paddle.fluid import compiler import paddle.fluid.dygraph as dygraph from paddle.fluid.dygraph.base import to_variable from paddle.fluid.dygraph.parallel import DataParallel -RUN_STEP = 10 +RUN_STEP = 5 DEFAULT_BATCH_SIZE = 2 +def my_print(class_name, log_str): + localtime = time.asctime(time.localtime(time.time())) + print_str = localtime + "\t" + class_name + "\t" + log_str + if six.PY2: + sys.stderr.write(pickle.dumps(print_str)) + else: + sys.stderr.buffer.write(pickle.dumps(print_str)) + + class TestDistRunnerBase(object): def get_model(self, batch_size=DEFAULT_BATCH_SIZE, @@ -51,11 +60,14 @@ class TestDistRunnerBase(object): trainers, sync_mode, dc_asgd=False, - current_endpoint=None): + current_endpoint=None, + nccl_comm_num=1): # NOTE: import fluid until runtime, or else forking processes will cause error. config = fluid.DistributeTranspilerConfig() config.enable_dc_asgd = dc_asgd config.sync_mode = sync_mode + if nccl_comm_num > 1: + config.nccl_comm_num = nccl_comm_num # config.runtime_split_send_recv = True t = fluid.DistributeTranspiler(config=config) t.transpile( @@ -80,7 +92,9 @@ class TestDistRunnerBase(object): place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) + my_print(type(self).__name__, "run pserver startup program done.") exe.run(pserver_prog) + my_print(type(self).__name__, "run pserver main program done.") def run_trainer(self, args): self.lr = args.lr @@ -95,17 +109,29 @@ class TestDistRunnerBase(object): self.get_model(batch_size=args.batch_size) if args.mem_opt: + my_print(type(self).__name__, "begin to run memory optimize") fluid.memory_optimize(fluid.default_main_program(), skip_grads=True) + my_print(type(self).__name__, "trainer run memory optimize done.") if args.update_method == "pserver": + my_print( + type(self).__name__, + "begin to run transpile on trainer with pserver mode") t = self.get_transpiler(args.trainer_id, fluid.default_main_program(), args.endpoints, args.trainers, args.sync_mode, args.dc_asgd) trainer_prog = t.get_trainer_program() + my_print( + type(self).__name__, + "get trainer program done with pserver mode.") elif args.update_method == "nccl2" or args.update_method == "nccl2_reduce_layer": # transpile for nccl2 config = fluid.DistributeTranspilerConfig() config.mode = "nccl2" + config.nccl_comm_num = args.nccl_comm_num + my_print( + type(self).__name__, + "begin to run transpile on trainer with nccl2 mode") nccl2_t = fluid.DistributeTranspiler(config=config) nccl2_t.transpile( args.trainer_id, @@ -113,6 +139,9 @@ class TestDistRunnerBase(object): startup_program=fluid.default_startup_program(), trainers=args.endpoints, current_endpoint=args.current_endpoint) + my_print( + type(self).__name__, + "get trainer program done. with nccl2 mode") trainer_prog = fluid.default_main_program() else: trainer_prog = fluid.default_main_program() @@ -125,6 +154,7 @@ class TestDistRunnerBase(object): exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) + my_print(type(self).__name__, "run worker startup program done.") exec_strategy = fluid.ExecutionStrategy() exec_strategy.num_threads = 1 @@ -135,6 +165,9 @@ class TestDistRunnerBase(object): build_stra.enable_inplace = False build_stra.memory_optimize = False + if args.enable_backward_deps: + build_stra.enable_backward_optimizer_op_deps = True + if args.use_reduce: build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce else: @@ -154,10 +187,21 @@ class TestDistRunnerBase(object): build_stra.num_trainers = 1 build_stra.trainer_id = 0 + my_print(type(self).__name__, "begin to compile with data parallel") binary = compiler.CompiledProgram(trainer_prog).with_data_parallel( loss_name=avg_cost.name, build_strategy=build_stra, exec_strategy=exec_strategy) + my_print(type(self).__name__, "program compiled with data parallel") + + if args.use_cuda and args.update_method == "nccl2": + # it just for test share_vars_from feature. + test_exe = fluid.ParallelExecutor( + use_cuda=True, + loss_name=avg_cost.name, + build_strategy=build_stra, + main_program=test_program, + share_vars_from=binary._executor) feed_var_list = [ var for var in trainer_prog.global_block().vars.values() @@ -178,6 +222,7 @@ class TestDistRunnerBase(object): else: return origin_batch + my_print(type(self).__name__, "begin to train on trainer") out_losses = [] for _ in six.moves.xrange(RUN_STEP): loss, = exe.run(binary, @@ -200,6 +245,7 @@ class TestParallelDyGraphRunnerBase(object): "train_one_loop should be implemented by the child classes.") def run_trainer(self, args): + seed = 90 device_id = int(os.getenv("FLAGS_selected_gpus", "0")) place = fluid.CUDAPlace(device_id) @@ -217,39 +263,48 @@ class TestParallelDyGraphRunnerBase(object): with fluid.dygraph.guard(place): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed + np.random.seed(seed) + import random + random.seed = seed model, train_reader, opt = self.get_model() - nranks = len(args.endpoints.split(",")) if args.endpoints else 1 + if args.update_method == "nccl2": - sys.stderr.write("") - model = dygraph.parallel.DataParallel(model) strategy = dygraph.parallel.ParallelStrategy() strategy.nranks = nranks strategy.local_rank = args.trainer_id strategy.trainer_endpoints = args.endpoints.split(",") strategy.current_endpoint = args.current_endpoint + my_print( + type(self).__name__, + "begin to prepare context in dygraph with nccl2") dygraph.parallel.prepare_context(strategy) + model = dygraph.parallel.DataParallel(model, strategy) + my_print(type(self).__name__, "model built in dygraph") out_losses = [] + my_print(type(self).__name__, "begin to run dygraph training") for step_id, data in enumerate(train_reader()): data = _get_data(data) if step_id == RUN_STEP: break loss = self.run_one_loop(model, opt, data) + if step_id % 10 == 0: + my_print( + type(self).__name__, + "loss at step %d: %f" % (step_id, loss)) + out_losses.append(loss.numpy()) - # FIXME(Yancey1989): scale the loss inplace - loss.stop_gradient = True - loss_scale = to_variable(np.array([nranks]).astype("float32")) - loss = loss / loss_scale + # FIXME(Yancey1989): scale the loss inplace + if args.update_method == "nccl2": + loss = model.scale_loss(loss) - out_losses.append(loss.numpy()) loss.backward() + if args.update_method == "nccl2": + model.apply_collective_grads() opt.minimize(loss) model.clear_gradients() - if six.PY2: - print(pickle.dumps(out_losses)) - else: - sys.stdout.buffer.write(pickle.dumps(out_losses)) + my_print(type(self).__name__, pickle.dumps(out_losses)) def runtime_main(test_class): @@ -264,6 +319,9 @@ def runtime_main(test_class): choices=["pserver", "nccl2", "local", "nccl2_reduce_layer"]) parser.add_argument('--trainer_id', type=int, required=False, default=0) parser.add_argument('--trainers', type=int, required=False, default=1) + parser.add_argument('--nccl_comm_num', type=int, required=False, default=1) + parser.add_argument( + '--enable_backward_deps', type=bool, required=False, default=1) parser.add_argument( '--current_endpoint', type=str, required=False, default="") parser.add_argument('--sync_mode', action='store_true') @@ -341,14 +399,18 @@ class TestDistBase(unittest.TestCase): self._lr = 0.001 self._use_dgc = False self._dygraph = False + self._nccl_comm_num = 1 self._setup_config() self._after_setup_config() + self._enable_backward_deps = False def _find_free_port(self): def __free_port(): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(('', 0)) + my_print( + type(self).__name__, "socket name: %s" % s.getsockname()[1]) return s.getsockname()[1] while True: @@ -379,11 +441,13 @@ class TestDistBase(unittest.TestCase): ps0_pipe = open("/tmp/ps0_err.log", "wb") ps1_pipe = open("/tmp/ps1_err.log", "wb") + my_print(type(self).__name__, "going to start pserver process 0") ps0_proc = subprocess.Popen( ps0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps0_pipe, env=required_envs) + my_print(type(self).__name__, "going to start pserver process 1") ps1_proc = subprocess.Popen( ps1_cmd.strip().split(" "), stdout=subprocess.PIPE, @@ -489,11 +553,13 @@ class TestDistBase(unittest.TestCase): tr0_pipe = open("/tmp/tr0_err.log", "wb") tr1_pipe = open("/tmp/tr1_err.log", "wb") + my_print(type(self).__name__, "going to start trainer process 0") tr0_proc = subprocess.Popen( tr0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=tr0_pipe, env=env0) + my_print(type(self).__name__, "going to start trainer process 1") tr1_proc = subprocess.Popen( tr1_cmd.strip().split(" "), stdout=subprocess.PIPE, @@ -525,16 +591,20 @@ class TestDistBase(unittest.TestCase): ps1.terminate() # print server log - with open("/tmp/ps0_err.log", "r") as fn: + ''' + with open("/tmp/ps0_err.log", "rb") as fn: sys.stderr.write("ps0 stderr: %s\n" % fn.read()) - with open("/tmp/ps1_err.log", "r") as fn: + with open("/tmp/ps1_err.log", "rb") as fn: sys.stderr.write("ps1 stderr: %s\n" % fn.read()) + ''' # print log - with open("/tmp/tr0_err.log", "r") as fn: + ''' + with open("/tmp/tr0_err.log", "rb") as fn: sys.stderr.write('trainer 0 stderr: %s\n' % fn.read()) - with open("/tmp/tr1_err.log", "r") as fn: + with open("/tmp/tr1_err.log", "rb") as fn: sys.stderr.write('trainer 1 stderr: %s\n' % fn.read()) + ''' return pickle.loads(tr0_out), pickle.loads(tr1_out) @@ -586,10 +656,19 @@ class TestDistBase(unittest.TestCase): if self._use_dgc: tr0_cmd += " --use_dgc" tr1_cmd += " --use_dgc" + + if self._nccl_comm_num > 1: + tr0_cmd += " --nccl_comm_num {}".format(self._nccl_comm_num) + tr1_cmd += " --nccl_comm_num {}".format(self._nccl_comm_num) + if self._mp_mode: env0 = {"FLAGS_selected_gpus": "0"} env1 = {"FLAGS_selected_gpus": "1"} + if self._enable_backward_deps: + tr0_cmd += " --enable_backward_deps 1" + tr1_cmd += " --enable_backward_deps 1" + env0.update(envs) env1.update(envs) @@ -598,11 +677,13 @@ class TestDistBase(unittest.TestCase): tr0_pipe = open("/tmp/tr0_err.log", "wb") tr1_pipe = open("/tmp/tr1_err.log", "wb") + my_print(type(self).__name__, "going to start process 0 with nccl2") tr0_proc = subprocess.Popen( tr0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=tr0_pipe, env=env0) + my_print(type(self).__name__, "going to start process 1 with nccl2") tr1_proc = subprocess.Popen( tr1_cmd.strip().split(" "), stdout=subprocess.PIPE, @@ -633,7 +714,7 @@ class TestDistBase(unittest.TestCase): "PYTHONPATH": os.getenv("PYTHONPATH", ""), "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""), "FLAGS_fraction_of_gpu_memory_to_use": "0.15", - "FLAGS_rpc_deadline": "5000", # 5sec to fail fast + "FLAGS_rpc_deadline": "30000", # 5sec to fail fast "FLAGS_cudnn_deterministic": "1", "http_proxy": "", "NCCL_P2P_DISABLE": "1" @@ -663,9 +744,6 @@ class TestDistBase(unittest.TestCase): local_loss = local_losses[step_id] tr0_loss = tr0_losses[step_id] tr1_loss = tr1_losses[step_id] - dist_loss = (np.array([tr0_loss]) + np.array([tr1_loss])) - if not self._dygraph: - # Parallel DyGraph already scaled the loss in training - dist_loss = dist_loss / 2 + dist_loss = (np.array([tr0_loss]) + np.array([tr1_loss])) / 2 print("=======", local_loss, ":", dist_loss[0], "=======") self.assertAlmostEqual(local_loss, dist_loss[0], delta=delta) diff --git a/python/paddle/fluid/tests/unittests/test_dist_mnist.py b/python/paddle/fluid/tests/unittests/test_dist_mnist.py index b9d2f6db394d949606530d18002af8e1b5f9f8e5..89bbc69fa889880ef6765ae7a00521e2e69ae7ac 100644 --- a/python/paddle/fluid/tests/unittests/test_dist_mnist.py +++ b/python/paddle/fluid/tests/unittests/test_dist_mnist.py @@ -26,42 +26,6 @@ class TestDistMnist2x2(TestDistBase): self.check_with_place("dist_mnist.py", delta=1e-5) -class TestDistMnistNCCL2(TestDistBase): - def _setup_config(self): - self._sync_mode = True - self._use_reduce = False - self._use_reader_alloc = False - self._nccl2_mode = True - - def test_dist_train(self): - import paddle.fluid as fluid - if fluid.core.is_compiled_with_cuda(): - self.check_with_place("dist_mnist.py", delta=1e-5) - - -class TestDistMnistNCCL2DGC(TestDistBase): - def _setup_config(self): - self._sync_mode = True - self._use_reduce = False - self._use_reader_alloc = False - self._nccl2_mode = True - self._use_dgc = True - - def test_dist_train(self): - import paddle.fluid as fluid - if fluid.core.is_compiled_with_cuda(): - self.check_with_place("dist_mnist.py", delta=1e-5) - - -class TestDistMnist2x2Lars(TestDistBase): - def _setup_config(self): - self._sync_mode = True - self._use_reduce = False - - def test_se_resnext(self): - self.check_with_place("dist_mnist_lars.py", delta=1e-5) - - class TestDistMnist2x2WithMemopt(TestDistBase): def _setup_config(self): self._sync_mode = True diff --git a/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py b/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py index 8b8fdcc887beb4879b2ce1101184dabe6f819acf..f473c435e59825486afe1669858971fcb772179e 100644 --- a/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py +++ b/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py @@ -51,7 +51,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer): self._conv2d = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, diff --git a/python/paddle/fluid/tests/unittests/test_expand_op.py b/python/paddle/fluid/tests/unittests/test_expand_op.py index 690875662e666aab63ac5eb62df0fb52823b8dff..847616034c6f2e0fabd30214ba814daf1b7bb032 100644 --- a/python/paddle/fluid/tests/unittests/test_expand_op.py +++ b/python/paddle/fluid/tests/unittests/test_expand_op.py @@ -34,6 +34,24 @@ class TestExpandOpRank1(OpTest): self.check_grad(['X'], 'Out') +class TestExpandOpRank1_tensor_attr(OpTest): + def setUp(self): + self.op_type = "expand" + self.inputs = { + 'X': np.random.random(12).astype("float32"), + 'expand_times_tensor': [('x1', np.ones((1)).astype('int32') * 2)] + } + self.attrs = {} + output = np.tile(self.inputs['X'], 2) + self.outputs = {'Out': output} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out', no_grad_set=set('x1')) + + class TestExpandOpRank2_Corner(OpTest): def setUp(self): self.op_type = "expand" @@ -49,6 +67,25 @@ class TestExpandOpRank2_Corner(OpTest): self.check_grad(['X'], 'Out') +class TestExpandOpRank2_Corner_tensor_attr(OpTest): + def setUp(self): + self.op_type = "expand" + self.inputs = { + 'X': np.random.random((12, 14)).astype("float32"), + 'expand_times_tensor': [('x1', np.ones((1)).astype('int32')), + ('x2', np.ones((1)).astype('int32'))] + } + self.attrs = {} + output = np.tile(self.inputs['X'], (1, 1)) + self.outputs = {'Out': output} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + class TestExpandOpRank2(OpTest): def setUp(self): self.op_type = "expand" @@ -64,6 +101,25 @@ class TestExpandOpRank2(OpTest): self.check_grad(['X'], 'Out') +class TestExpandOpRank2_attr_tensor(OpTest): + def setUp(self): + self.op_type = "expand" + self.inputs = { + 'X': np.random.random((12, 14)).astype("float32"), + 'expand_times_tensor': [('x1', np.ones((1)).astype('int32') * 2), + ('x2', np.ones((1)).astype('int32') * 3)] + } + self.attrs = {} + output = np.tile(self.inputs['X'], (2, 3)) + self.outputs = {'Out': output} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + class TestExpandOpRank3_Corner(OpTest): def setUp(self): self.op_type = "expand" diff --git a/python/paddle/fluid/tests/unittests/test_fake_quantize_op.py b/python/paddle/fluid/tests/unittests/test_fake_quantize_op.py index 8d82438c15c7853f39566937e4192ef88a4f79ae..8fe814dc50d486c8a59c74f965f7e9c5e9b40d7c 100644 --- a/python/paddle/fluid/tests/unittests/test_fake_quantize_op.py +++ b/python/paddle/fluid/tests/unittests/test_fake_quantize_op.py @@ -90,46 +90,6 @@ class TestFakeQuantizeRangeAbsMaxOp(OpTest): self.check_output() -class TestFakeQuantizeMovingOp(OpTest): - def setUp(self): - self.op_type = "fake_quantize_moving_average_abs_max" - self.attrs = { - 'bit_length': int(5), - 'moving_rate': float(0.9), - 'is_test': False - } - accum = np.zeros(1).astype("float32") - accum[0] = 1 - state = np.zeros(1).astype("float32") - state[0] = 1 - scale = np.zeros(1).astype("float32") - scale[0] = 0.001 - self.inputs = { - 'X': np.random.random((8, 16, 7, 7)).astype("float32"), - 'InScale': scale, - 'InAccum': accum, - 'InState': state, - } - - out_accum = np.zeros(1).astype("float32") - out_state = np.zeros(1).astype("float32") - out_scale = np.zeros(1).astype("float32") - out_accum[0] = self.attrs['moving_rate'] * accum[0] + np.max( - np.abs(self.inputs['X'])).astype("float32") - out_state[0] = self.attrs['moving_rate'] * state[0] + 1 - out_scale = out_accum / out_state - self.outputs = { - 'Out': np.round(self.inputs['X'] / out_scale * ( - (1 << (self.attrs['bit_length'] - 1)) - 1)), - 'OutAccum': out_accum, - 'OutState': out_state, - 'OutScale': out_scale, - } - - def test_check_output(self): - self.check_output() - - class TestMovingAverageAbsMaxScaleOp(OpTest): def setUp(self): self.op_type = "moving_average_abs_max_scale" @@ -193,5 +153,62 @@ class TestFakeQuantizeRangeAbsMaxOp2(OpTest): self.check_output(no_check_set=set(['OutScale', 'OutScales'])) +class TestMovingOpBase(OpTest): + def setUp(self): + self.init_type() + self.attrs = { + 'bit_length': int(5), + 'moving_rate': float(0.9), + 'is_test': False + } + accum = np.zeros(1).astype("float32") + accum[0] = 1 + state = np.zeros(1).astype("float32") + state[0] = 1 + scale = np.zeros(1).astype("float32") + scale[0] = 0.001 + self.inputs = { + 'X': np.random.random((8, 16, 7, 7)).astype("float32"), + 'InScale': scale, + 'InAccum': accum, + 'InState': state, + } + + out_accum = np.zeros(1).astype("float32") + out_state = np.zeros(1).astype("float32") + out_scale = np.zeros(1).astype("float32") + out_accum[0] = self.attrs['moving_rate'] * accum[0] + np.max( + np.abs(self.inputs['X'])).astype("float32") + out_state[0] = self.attrs['moving_rate'] * state[0] + 1 + out_scale = out_accum / out_state + out_data = self.calc_output(out_scale) + self.outputs = { + 'Out': out_data, + 'OutAccum': out_accum, + 'OutState': out_state, + 'OutScale': out_scale, + } + + def init_type(self): + self.op_type = "fake_quantize_moving_average_abs_max" + + def calc_output(self, out_scale): + return np.round(self.inputs['X'] / out_scale * ( + (1 << (self.attrs['bit_length'] - 1)) - 1)) + + def test_check_output(self): + self.check_output() + + +class TestFakeQuantDequantMovingOp(TestMovingOpBase): + def init_type(self): + self.op_type = "fake_quantize_dequantize_moving_average_abs_max" + + def calc_output(self, out_scale): + range_v = (1 << (self.attrs['bit_length'] - 1)) - 1 + return np.round(self.inputs['X'] / out_scale * + range_v) * out_scale / range_v + + if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_gather_op.py b/python/paddle/fluid/tests/unittests/test_gather_op.py index bd5785aa55af241fe42a1ae2c550dbdb980f42e2..119f64ce7343819ff6c2f22e2d23c3900ac24691 100644 --- a/python/paddle/fluid/tests/unittests/test_gather_op.py +++ b/python/paddle/fluid/tests/unittests/test_gather_op.py @@ -23,8 +23,11 @@ class TestGatherOp(OpTest): def setUp(self): self.op_type = "gather" self.config() - xnp = np.random.random(self.x_shape).astype("float32") - self.inputs = {'X': xnp, 'Index': np.array(self.index).astype("int32")} + xnp = np.random.random(self.x_shape).astype(self.x_type) + self.inputs = { + 'X': xnp, + 'Index': np.array(self.index).astype(self.index_type) + } self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]} def test_check_output(self): @@ -34,14 +37,73 @@ class TestGatherOp(OpTest): self.check_grad(['X'], 'Out') def config(self): + """ + For multi-dimension input + """ self.x_shape = (10, 20) + self.x_type = "float32" self.index = [1, 3, 5] + self.index_type = "int32" class TestCase1(TestGatherOp): def config(self): + """ + For one dimension input + """ self.x_shape = (10) + self.x_type = "float32" self.index = [1, 3, 5] + self.index_type = "int32" + + +class TestCase2(TestGatherOp): + def config(self): + """ + For int64_t index type + """ + self.x_shape = (10) + self.x_type = "float32" + self.index = [1, 3, 5] + self.index_type = "int64" + + +class TestCase3(TestGatherOp): + def config(self): + """ + For other input type + """ + self.x_shape = (10, 20) + self.x_type = "double" + self.index = [1, 3, 5] + self.index_type = "int64" + + +class TestCase4(TestGatherOp): + def config(self): + self.x_shape = (10, 20) + self.attrs = {'overwrite': False} + self.x_type = "double" + self.index = [1, 1] + self.index_type = "int32" + + +class TestCase5(TestGatherOp): + def config(self): + self.x_shape = (10, 20) + self.attrs = {'overwrite': False} + self.x_type = "float" + self.index = [1, 1, 3] + self.index_type = "int32" + + +class TestCase6(TestGatherOp): + def config(self): + self.x_shape = (10, 20) + self.attrs = {'overwrite': True} + self.x_type = "float" + self.index = [1, 3] + self.index_type = "int32" if __name__ == "__main__": diff --git a/python/paddle/fluid/tests/unittests/test_generate_proposal_labels_op.py b/python/paddle/fluid/tests/unittests/test_generate_proposal_labels_op.py index 5f6328707fd80ec8f11b96cc65e2dcaf44496d58..406c255970a52d50c14efb685f55c89947958339 100644 --- a/python/paddle/fluid/tests/unittests/test_generate_proposal_labels_op.py +++ b/python/paddle/fluid/tests/unittests/test_generate_proposal_labels_op.py @@ -22,10 +22,10 @@ import paddle.fluid as fluid from op_test import OpTest -def generate_proposal_labels_in_python(rpn_rois, gt_classes, is_crowd, gt_boxes, - im_info, batch_size_per_im, fg_fraction, - fg_thresh, bg_thresh_hi, bg_thresh_lo, - bbox_reg_weights, class_nums): +def generate_proposal_labels_in_python( + rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, batch_size_per_im, + fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, + class_nums, is_cls_agnostic, is_cascade_rcnn): rois = [] labels_int32 = [] bbox_targets = [] @@ -36,13 +36,12 @@ def generate_proposal_labels_in_python(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info), 'batch size of rpn_rois and ground_truth is not matched' for im_i in range(len(im_info)): - frcn_blobs = _sample_rois( - rpn_rois[im_i], gt_classes[im_i], is_crowd[im_i], gt_boxes[im_i], - im_info[im_i], batch_size_per_im, fg_fraction, fg_thresh, - bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, class_nums) - + frcn_blobs = _sample_rois(rpn_rois[im_i], gt_classes[im_i], + is_crowd[im_i], gt_boxes[im_i], im_info[im_i], + batch_size_per_im, fg_fraction, fg_thresh, + bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, + class_nums, is_cls_agnostic, is_cascade_rcnn) lod.append(frcn_blobs['rois'].shape[0]) - rois.append(frcn_blobs['rois']) labels_int32.append(frcn_blobs['labels_int32']) bbox_targets.append(frcn_blobs['bbox_targets']) @@ -54,7 +53,8 @@ def generate_proposal_labels_in_python(rpn_rois, gt_classes, is_crowd, gt_boxes, def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, batch_size_per_im, fg_fraction, fg_thresh, bg_thresh_hi, - bg_thresh_lo, bbox_reg_weights, class_nums): + bg_thresh_lo, bbox_reg_weights, class_nums, is_cls_agnostic, + is_cascade_rcnn): rois_per_image = int(batch_size_per_im) fg_rois_per_im = int(np.round(fg_fraction * rois_per_image)) @@ -62,7 +62,8 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, im_scale = im_info[2] inv_im_scale = 1. / im_scale rpn_rois = rpn_rois * inv_im_scale - + if is_cascade_rcnn: + rpn_rois = rpn_rois[gt_boxes.shape[0]:, :] boxes = np.vstack([gt_boxes, rpn_rois]) gt_overlaps = np.zeros((boxes.shape[0], class_nums)) box_to_gt_ind_map = np.zeros((boxes.shape[0]), dtype=np.int32) @@ -87,26 +88,37 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, max_overlaps = gt_overlaps.max(axis=1) max_classes = gt_overlaps.argmax(axis=1) - # Foreground - fg_inds = np.where(max_overlaps >= fg_thresh)[0] - fg_rois_per_this_image = np.minimum(fg_rois_per_im, fg_inds.shape[0]) - # Sample foreground if there are too many - # if fg_inds.shape[0] > fg_rois_per_this_image: - # fg_inds = np.random.choice( - # fg_inds, size=fg_rois_per_this_image, replace=False) - fg_inds = fg_inds[:fg_rois_per_this_image] - - # Background - bg_inds = np.where((max_overlaps < bg_thresh_hi) & (max_overlaps >= - bg_thresh_lo))[0] - bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image - bg_rois_per_this_image = np.minimum(bg_rois_per_this_image, - bg_inds.shape[0]) - # Sample background if there are too many - # if bg_inds.shape[0] > bg_rois_per_this_image: - # bg_inds = np.random.choice( - # bg_inds, size=bg_rois_per_this_image, replace=False) - bg_inds = bg_inds[:bg_rois_per_this_image] + # Cascade RCNN Decode Filter + if is_cascade_rcnn: + ws = boxes[:, 2] - boxes[:, 0] + 1 + hs = boxes[:, 3] - boxes[:, 1] + 1 + keep = np.where((ws > 0) & (hs > 0))[0] + boxes = boxes[keep] + fg_inds = np.where(max_overlaps >= fg_thresh)[0] + bg_inds = np.where((max_overlaps < bg_thresh_hi) & (max_overlaps >= + bg_thresh_lo))[0] + fg_rois_per_this_image = fg_inds.shape[0] + bg_rois_per_this_image = bg_inds.shape[0] + else: + # Foreground + fg_inds = np.where(max_overlaps >= fg_thresh)[0] + fg_rois_per_this_image = np.minimum(fg_rois_per_im, fg_inds.shape[0]) + # Sample foreground if there are too many + if fg_inds.shape[0] > fg_rois_per_this_image: + fg_inds = np.random.choice( + fg_inds, size=fg_rois_per_this_image, replace=False) + fg_inds = fg_inds[:fg_rois_per_this_image] + # Background + bg_inds = np.where((max_overlaps < bg_thresh_hi) & (max_overlaps >= + bg_thresh_lo))[0] + bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image + bg_rois_per_this_image = np.minimum(bg_rois_per_this_image, + bg_inds.shape[0]) + # Sample background if there are too many + if bg_inds.shape[0] > bg_rois_per_this_image: + bg_inds = np.random.choice( + bg_inds, size=bg_rois_per_this_image, replace=False) + bg_inds = bg_inds[:bg_rois_per_this_image] keep_inds = np.append(fg_inds, bg_inds) sampled_labels = max_classes[keep_inds] @@ -114,14 +126,12 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, sampled_boxes = boxes[keep_inds] sampled_gts = gt_boxes[box_to_gt_ind_map[keep_inds]] sampled_gts[fg_rois_per_this_image:, :] = gt_boxes[0] - bbox_label_targets = _compute_targets(sampled_boxes, sampled_gts, sampled_labels, bbox_reg_weights) - bbox_targets, bbox_inside_weights = _expand_bbox_targets(bbox_label_targets, - class_nums) + bbox_targets, bbox_inside_weights = _expand_bbox_targets( + bbox_label_targets, class_nums, is_cls_agnostic) bbox_outside_weights = np.array( bbox_inside_weights > 0, dtype=bbox_inside_weights.dtype) - # Scale rois sampled_rois = sampled_boxes * im_scale @@ -192,19 +202,22 @@ def _box_to_delta(ex_boxes, gt_boxes, weights): return targets -def _expand_bbox_targets(bbox_targets_input, class_nums): +def _expand_bbox_targets(bbox_targets_input, class_nums, is_cls_agnostic): class_labels = bbox_targets_input[:, 0] fg_inds = np.where(class_labels > 0)[0] - - bbox_targets = np.zeros((class_labels.shape[0], 4 * class_nums)) + #if is_cls_agnostic: + # class_labels = [1 if ll > 0 else 0 for ll in class_labels] + # class_labels = np.array(class_labels, dtype=np.int32) + # class_nums = 2 + bbox_targets = np.zeros((class_labels.shape[0], 4 * class_nums + if not is_cls_agnostic else 4 * 2)) bbox_inside_weights = np.zeros(bbox_targets.shape) for ind in fg_inds: - class_label = int(class_labels[ind]) + class_label = int(class_labels[ind]) if not is_cls_agnostic else 1 start_ind = class_label * 4 end_ind = class_label * 4 + 4 bbox_targets[ind, start_ind:end_ind] = bbox_targets_input[ind, 1:] bbox_inside_weights[ind, start_ind:end_ind] = (1.0, 1.0, 1.0, 1.0) - return bbox_targets, bbox_inside_weights @@ -228,7 +241,9 @@ class TestGenerateProposalLabelsOp(OpTest): 'bg_thresh_lo': self.bg_thresh_lo, 'bbox_reg_weights': self.bbox_reg_weights, 'class_nums': self.class_nums, - 'use_random': False + 'use_random': False, + 'is_cls_agnostic': self.is_cls_agnostic, + 'is_cascade_rcnn': self.is_cascade_rcnn } self.outputs = { 'Rois': (self.rois, [self.lod]), @@ -252,12 +267,15 @@ class TestGenerateProposalLabelsOp(OpTest): self.bg_thresh_hi = 0.5 self.bg_thresh_lo = 0.0 self.bbox_reg_weights = [0.1, 0.1, 0.2, 0.2] - self.class_nums = 81 + #self.class_nums = 81 + self.is_cls_agnostic = False #True + self.is_cascade_rcnn = True + self.class_nums = 2 if self.is_cls_agnostic else 81 def init_test_input(self): np.random.seed(0) gt_nums = 6 # Keep same with batch_size_per_im for unittest - proposal_nums = 2000 #self.batch_size_per_im - gt_nums + proposal_nums = 2000 if not self.is_cascade_rcnn else 512 #self.batch_size_per_im - gt_nums images_shape = [[64, 64]] self.im_info = np.ones((len(images_shape), 3)).astype(np.float32) for i in range(len(images_shape)): @@ -280,7 +298,8 @@ class TestGenerateProposalLabelsOp(OpTest): self.rpn_rois, self.gt_classes, self.is_crowd, self.gt_boxes, self.im_info, self.batch_size_per_im, self.fg_fraction, self.fg_thresh, self.bg_thresh_hi, self.bg_thresh_lo, - self.bbox_reg_weights, self.class_nums + self.bbox_reg_weights, self.class_nums, + self.is_cls_agnostic, self.is_cascade_rcnn ) self.rois = np.vstack(self.rois) self.labels_int32 = np.hstack(self.labels_int32) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_basic.py b/python/paddle/fluid/tests/unittests/test_imperative_basic.py index 8404a57eb85a30edda6889150e588cab783be685..afa21a375a4da29c1ea964eb66f792f0cc7a0356 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_basic.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_basic.py @@ -34,20 +34,6 @@ class MyLayer(fluid.Layer): return [x] -class MyPyLayer(fluid.PyLayer): - def __init__(self): - super(MyPyLayer, self).__init__() - - @staticmethod - def forward(inputs): - return np.tanh(inputs[0]) - - @staticmethod - def backward(inputs): - inp, out, dout = inputs - return np.array(dout) * (1 - np.square(np.array(out))) - - class MLP(fluid.Layer): def __init__(self, name_scope): super(MLP, self).__init__(name_scope) @@ -81,7 +67,7 @@ class SimpleRNNCell(fluid.Layer): self._dtype = core.VarDesc.VarType.FP32 self.param_attr = param_attr - def build_once(self, inputs, pre_hidden): + def _build_once(self, inputs, pre_hidden): i2h_param_shape = [self.step_input_size, self.hidden_size] h2h_param_shape = [self.hidden_size, self.hidden_size] h2o_param_shape = [self.output_size, self.hidden_size] @@ -201,8 +187,21 @@ class TestImperative(unittest.TestCase): ret = fluid.layers.sums(inputs) loss = fluid.layers.reduce_sum(ret) loss.backward() + with fluid.dygraph.guard(): + inputs2 = [] + for _ in range(10): + inputs2.append(fluid.dygraph.base.to_variable(x)) + ret2 = fluid.layers.sums(inputs2) + loss2 = fluid.layers.reduce_sum(ret2) + backward_strategy = fluid.dygraph.BackwardStrategy() + backward_strategy.sort_sum_gradient = True + loss2.backward(backward_strategy) + self.assertTrue(np.allclose(ret.numpy(), x * 10)) self.assertTrue(np.allclose(inputs[0].gradient(), x)) + self.assertTrue(np.allclose(ret2.numpy(), x * 10)) + a = inputs2[0].gradient() + self.assertTrue(np.allclose(inputs2[0].gradient(), x)) def test_layer(self): with fluid.dygraph.guard(): @@ -211,75 +210,6 @@ class TestImperative(unittest.TestCase): l = fluid.Layer("l") self.assertRaises(NotImplementedError, l.forward, []) - def test_pylayer_func_id(self): - - with fluid.dygraph.guard(): - - class PyLayer1(fluid.PyLayer): - def __init__(self): - super(PyLayer1, self).__init__() - - @staticmethod - def forward(input): - return input - - @staticmethod - def backward(input): - return input - - class PyLayer2(fluid.PyLayer): - def __init__(self): - super(PyLayer2, self).__init__() - - @staticmethod - def forward(input): - return input - - @staticmethod - def backward(input): - return input - - py_layer_1 = PyLayer1() - py_layer_2 = PyLayer2() - py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2]))) - py_layer_2(fluid.dygraph.base.to_variable(np.ones([2, 2]))) - id = py_layer_1.forward_id - self.assertGreater(id, 0) - self.assertEqual(py_layer_1.backward_id, id + 1) - self.assertEqual(py_layer_2.forward_id, id + 2) - self.assertEqual(py_layer_2.backward_id, id + 3) - py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2]))) - self.assertEqual(py_layer_1.forward_id, id) - - def test_pylayer(self): - np_inp = np.ones([2, 2], np.float32) - with fluid.dygraph.guard(): - my_py_layer = MyPyLayer() - var_inp = fluid.dygraph.base.to_variable(np_inp) - outs = my_py_layer(var_inp) - dy_out = np.sum(outs[0].numpy()) - outs[0].backward() - dy_grad = var_inp.gradient() - - with new_program_scope(): - inp = fluid.layers.data( - name="inp", shape=[2, 2], append_batch_size=False) - # TODO(panyx0718): Paddle doesn't diff against data `inp`. - x1 = inp * 1 - # TODO(panyx0718): If reduce_sum is skipped, the result is wrong. - x = fluid.layers.reduce_sum(fluid.layers.tanh(x1)) - param_grads = fluid.backward.append_backward( - x, parameter_list=[x1.name])[0] - exe = fluid.Executor(fluid.CPUPlace( - ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) - - static_out, static_grad = exe.run( - feed={inp.name: np_inp}, - fetch_list=[x.name, param_grads[1].name]) - - self.assertTrue(np.allclose(dy_out, static_out)) - self.assertTrue(np.allclose(dy_grad, static_grad)) - def test_layer_in_out(self): np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32) with fluid.dygraph.guard(): @@ -291,6 +221,17 @@ class TestImperative(unittest.TestCase): x.backward() dy_grad = l._x_for_debug.gradient() + with fluid.dygraph.guard(): + var_inp2 = fluid.dygraph.base.to_variable(np_inp) + l2 = MyLayer("my_layer") + x2 = l2(var_inp2)[0] + self.assertIsNotNone(x2) + dy_out2 = x2.numpy() + backward_strategy = fluid.dygraph.BackwardStrategy() + backward_strategy.sort_sum_gradient = True + x2.backward(backward_strategy) + dy_grad2 = l2._x_for_debug.gradient() + with new_program_scope(): inp = fluid.layers.data( name="inp", shape=[3], append_batch_size=False) @@ -307,6 +248,8 @@ class TestImperative(unittest.TestCase): self.assertTrue(np.allclose(dy_out, static_out)) self.assertTrue(np.allclose(dy_grad, static_grad)) + self.assertTrue(np.allclose(dy_out2, static_out)) + self.assertTrue(np.allclose(dy_grad2, static_grad)) def test_mlp(self): np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32) @@ -318,6 +261,16 @@ class TestImperative(unittest.TestCase): out.backward() dy_grad = mlp._fc1._w.gradient() + with fluid.dygraph.guard(): + var_inp2 = fluid.dygraph.base.to_variable(np_inp) + mlp2 = MLP("mlp") + out2 = mlp2(var_inp2) + dy_out2 = out2.numpy() + backward_strategy = fluid.dygraph.BackwardStrategy() + backward_strategy.sort_sum_gradient = True + out2.backward(backward_strategy) + dy_grad2 = mlp2._fc1._w.gradient() + with new_program_scope(): inp = fluid.layers.data( name="inp", shape=[2, 2], append_batch_size=False) @@ -335,6 +288,8 @@ class TestImperative(unittest.TestCase): self.assertTrue(np.allclose(dy_out, static_out)) self.assertTrue(np.allclose(dy_grad, static_grad)) + self.assertTrue(np.allclose(dy_out2, static_out)) + self.assertTrue(np.allclose(dy_grad2, static_grad)) params = mlp.parameters(True) self.assertEqual("mlp/MLP_0/FC_0.w_0", params[0].name) @@ -413,6 +368,19 @@ class TestImperative(unittest.TestCase): dy_grad_h2h = simple_rnn._cell._h2h_w.gradient() dy_grad_i2h = simple_rnn._cell._i2h_w.gradient() + with fluid.dygraph.guard(): + var_inp2 = fluid.dygraph.base.to_variable(np_inp) + var_inp2 = fluid.layers.reshape(var_inp2, shape=[1, 4, 3]) + simple_rnn2 = SimpleRNN("simple_rnn") + outs2, pre_hiddens2 = simple_rnn2.forward(var_inp2) + dy_out2 = outs2[3].numpy() + backward_strategy = fluid.dygraph.BackwardStrategy() + backward_strategy.sort_sum_gradient = True + outs2[3].backward(backward_strategy) + dy_grad_h2o2 = simple_rnn2._cell._h2o_w.gradient() + dy_grad_h2h2 = simple_rnn2._cell._h2h_w.gradient() + dy_grad_i2h2 = simple_rnn2._cell._i2h_w.gradient() + with new_program_scope(): inp = fluid.layers.data( name="inp", shape=[1, 4, 3], append_batch_size=False) @@ -427,10 +395,15 @@ class TestImperative(unittest.TestCase): outs[3].name, param_grads[0][1].name, param_grads[1][1].name, param_grads[2][1].name ]) + self.assertTrue(np.allclose(dy_out, static_out)) self.assertTrue(np.allclose(dy_grad_h2o, static_grad_h2o)) self.assertTrue(np.allclose(dy_grad_h2h, static_grad_h2h)) self.assertTrue(np.allclose(dy_grad_i2h, static_grad_i2h)) + self.assertTrue(np.allclose(dy_out2, static_out)) + self.assertTrue(np.allclose(dy_grad_h2o2, static_grad_h2o)) + self.assertTrue(np.allclose(dy_grad_h2h2, static_grad_h2h)) + self.assertTrue(np.allclose(dy_grad_i2h2, static_grad_i2h)) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py b/python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py index 889e7c0fa6c0995ef821dd8ca2020619e2bacc97..25d490f6797f3ae63308eb3e449d371864d9b28f 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py @@ -18,14 +18,13 @@ import numpy as np import paddle import paddle.fluid as fluid from paddle.fluid.optimizer import SGDOptimizer -from paddle.fluid import Conv2D, Pool2D, FC +from paddle.fluid import Conv2D, Pool2D, FC, core from paddle.fluid.dygraph.base import to_variable class SimpleImgConvPool(fluid.Layer): def __init__(self, name_scope, - num_channels, num_filters, filter_size, pool_size, @@ -45,7 +44,6 @@ class SimpleImgConvPool(fluid.Layer): self._conv2d = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, @@ -76,10 +74,10 @@ class MNIST(fluid.Layer): super(MNIST, self).__init__(name_scope) self._simple_img_conv_pool_1 = SimpleImgConvPool( - self.full_name(), 1, 20, 5, 2, 2, act="relu") + self.full_name(), 20, 5, 2, 2, act="relu") self._simple_img_conv_pool_2 = SimpleImgConvPool( - self.full_name(), 20, 50, 5, 2, 2, act="relu") + self.full_name(), 50, 5, 2, 2, act="relu") pool_2_shape = 50 * 4 * 4 SIZE = 10 @@ -99,9 +97,19 @@ class MNIST(fluid.Layer): class TestDygraphCheckpoint(unittest.TestCase): + def reader_decorator(self, reader): + def _reader_imple(): + for item in reader(): + image = np.array(item[0]).reshape(1, 28, 28) + label = np.array(item[1]).astype('int64').reshape(1) + yield image, label + + return _reader_imple + def test_save_load_persistables(self): seed = 90 epoch_num = 1 + batch_size = 128 with fluid.dygraph.guard(): fluid.default_startup_program().random_seed = seed @@ -109,22 +117,21 @@ class TestDygraphCheckpoint(unittest.TestCase): mnist = MNIST("mnist") sgd = SGDOptimizer(learning_rate=1e-3) - train_reader = paddle.batch( - paddle.dataset.mnist.train(), batch_size=128, drop_last=True) + + batch_py_reader = fluid.io.PyReader(capacity=1) + batch_py_reader.decorate_sample_list_generator( + paddle.batch( + self.reader_decorator(paddle.dataset.mnist.train()), + batch_size=batch_size, + drop_last=True), + places=fluid.CPUPlace()) dy_param_init_value = {} - step = 0 for epoch in range(epoch_num): - for batch_id, data in enumerate(train_reader()): - dy_x_data = np.array( - [x[0].reshape(1, 28, 28) - for x in data]).astype('float32') - y_data = np.array( - [x[1] for x in data]).astype('int64').reshape(128, 1) - - img = to_variable(dy_x_data) - label = to_variable(y_data) + for batch_id, data in enumerate(batch_py_reader()): + img = data[0] + label = data[1] label.stop_gradient = True cost = mnist(img) @@ -142,7 +149,7 @@ class TestDygraphCheckpoint(unittest.TestCase): for param in mnist.parameters(): dy_param_init_value[param.name] = param.numpy() - restore = fluid.dygraph.load_persistables("save_dir") + restore, _ = fluid.dygraph.load_persistables("save_dir") mnist.load_dict(restore) self.assertEqual(len(dy_param_init_value), len(restore)) @@ -153,9 +160,7 @@ class TestDygraphCheckpoint(unittest.TestCase): self.assertTrue(np.isfinite(value.numpy().all())) self.assertFalse(np.isnan(value.numpy().any())) - step += 1 - - if step > 10: + if batch_id > 10: break diff --git a/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py b/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py index ca2cffa9c75cc851f0911cb0063f4e82bb2a41eb..daf8cc00d434e6843b224a5ef8de4176105bbf73 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py @@ -258,7 +258,35 @@ class TestDygraphDeepCF(unittest.TestCase): dy_loss = loss.numpy() sys.stderr.write('dynamic loss: %s %s\n' % (slice, dy_loss)) + with fluid.dygraph.guard(): + fluid.default_startup_program().random_seed = seed + fluid.default_main_program().random_seed = seed + + deepcf2 = DeepCF('deepcf', num_users, num_items, matrix) + adam2 = fluid.optimizer.AdamOptimizer(0.01) + backward_strategy = fluid.dygraph.BackwardStrategy() + backward_strategy.sort_sum_gradient = True + for e in range(NUM_EPOCHES): + sys.stderr.write('epoch %d\n' % e) + for slice in range(0, BATCH_SIZE * NUM_BATCHES, BATCH_SIZE): + if slice + BATCH_SIZE >= users_np.shape[0]: + break + prediction2 = deepcf2( + to_variable(users_np[slice:slice + BATCH_SIZE]), + to_variable(items_np[slice:slice + BATCH_SIZE])) + loss2 = fluid.layers.reduce_sum( + fluid.layers.log_loss(prediction2, + to_variable(labels_np[ + slice:slice + BATCH_SIZE]))) + loss2.backward(backward_strategy) + adam2.minimize(loss2) + deepcf2.clear_gradients() + dy_loss2 = loss2.numpy() + sys.stderr.write('dynamic loss: %s %s\n' % + (slice, dy_loss2)) + self.assertEqual(static_loss, dy_loss) + self.assertEqual(static_loss, dy_loss2) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/unittests/test_imperative_gan.py b/python/paddle/fluid/tests/unittests/test_imperative_gan.py index 5d773ec1c9db160cd63a28c634043037260e0b82..7e8cebab44eee1889327ec78f8007ed28fe38981 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_gan.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_gan.py @@ -170,11 +170,59 @@ class TestDygraphGAN(unittest.TestCase): dy_g_loss = g_loss.numpy() dy_d_loss = d_loss.numpy() + dy_params2 = dict() + with fluid.dygraph.guard(): + fluid.default_startup_program().random_seed = seed + fluid.default_main_program().random_seed = seed + + backward_strategy = fluid.dygraph.BackwardStrategy() + backward_strategy.sort_sum_gradient = True + discriminator2 = Discriminator("d") + generator2 = Generator("g") + sgd2 = SGDOptimizer(learning_rate=1e-3) + + d_real2 = discriminator2(to_variable(np.ones([2, 1], np.float32))) + d_loss_real2 = fluid.layers.reduce_mean( + fluid.layers.sigmoid_cross_entropy_with_logits( + x=d_real2, label=to_variable(np.ones([2, 1], np.float32)))) + + d_fake2 = discriminator2( + generator2(to_variable(np.ones([2, 2], np.float32)))) + d_loss_fake2 = fluid.layers.reduce_mean( + fluid.layers.sigmoid_cross_entropy_with_logits( + x=d_fake2, label=to_variable(np.zeros([2, 1], np.float32)))) + + d_loss2 = d_loss_real2 + d_loss_fake2 + d_loss2.backward(backward_strategy) + sgd2.minimize(d_loss2) + discriminator2.clear_gradients() + generator2.clear_gradients() + + d_fake2 = discriminator2( + generator2(to_variable(np.ones([2, 2], np.float32)))) + g_loss2 = fluid.layers.reduce_mean( + fluid.layers.sigmoid_cross_entropy_with_logits( + x=d_fake2, label=to_variable(np.ones([2, 1], np.float32)))) + g_loss2.backward(backward_strategy) + sgd2.minimize(g_loss2) + for p in discriminator2.parameters(): + dy_params2[p.name] = p.numpy() + for p in generator.parameters(): + dy_params2[p.name] = p.numpy() + + dy_g_loss2 = g_loss2.numpy() + dy_d_loss2 = d_loss2.numpy() + self.assertEqual(dy_g_loss, static_g_loss) self.assertEqual(dy_d_loss, static_d_loss) for k, v in six.iteritems(dy_params): self.assertTrue(np.allclose(v, static_params[k])) + self.assertEqual(dy_g_loss2, static_g_loss) + self.assertEqual(dy_d_loss2, static_d_loss) + for k, v in six.iteritems(dy_params2): + self.assertTrue(np.allclose(v, static_params[k])) + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_imperative_gnn.py b/python/paddle/fluid/tests/unittests/test_imperative_gnn.py index 234fcd60404286977309083257c24d941db77449..8531eda86978302f4014e11577f7055f1ef156b6 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_gnn.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_gnn.py @@ -101,11 +101,11 @@ class TestDygraphGNN(unittest.TestCase): ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) exe.run(startup) static_loss = exe.run(feed={ - 'features': np.zeros( + 'features': np.ones( [1, 100, 50], dtype=np.float32), - 'adj': np.zeros( + 'adj': np.ones( [1, 100, 100], dtype=np.float32), - 'labels': np.zeros( + 'labels': np.ones( [100, 1], dtype=np.int64) }, fetch_list=[loss])[0] @@ -117,10 +117,10 @@ class TestDygraphGNN(unittest.TestCase): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed - features = np.zeros([1, 100, 50], dtype=np.float32) + features = np.ones([1, 100, 50], dtype=np.float32) # Use selected rows when it's supported. - adj = np.zeros([1, 100, 100], dtype=np.float32) - labels = np.zeros([100, 1], dtype=np.int64) + adj = np.ones([1, 100, 100], dtype=np.float32) + labels = np.ones([100, 1], dtype=np.int64) model = GCN('test_gcn', 50) logits = model(to_variable(features), to_variable(adj)) @@ -130,11 +130,39 @@ class TestDygraphGNN(unittest.TestCase): loss = fluid.layers.softmax_with_cross_entropy(logits, to_variable(labels)) loss = fluid.layers.reduce_sum(loss) + loss.backward() adam = AdamOptimizer(learning_rate=1e-3) + adam.minimize(loss) - self.assertEqual(static_loss, loss.numpy()) - self.assertTrue(np.allclose(static_weight, model.gc.weight.numpy())) - sys.stderr.write('%s %s\n' % (static_loss, loss.numpy())) + model.clear_gradients() + + with fluid.dygraph.guard(): + fluid.default_startup_program().random_seed = seed + fluid.default_main_program().random_seed = seed + + features2 = np.ones([1, 100, 50], dtype=np.float32) + # Use selected rows when it's supported. + adj2 = np.ones([1, 100, 100], dtype=np.float32) + labels2 = np.ones([100, 1], dtype=np.int64) + + model2 = GCN('test_gcn', 50) + logits2 = model2(to_variable(features2), to_variable(adj2)) + logits2 = fluid.layers.reshape(logits2, logits2.shape[1:]) + # In other example, it's nll with log_softmax. However, paddle's + # log_loss only supports binary classification now. + loss2 = fluid.layers.softmax_with_cross_entropy( + logits2, to_variable(labels2)) + loss2 = fluid.layers.reduce_sum(loss2) + loss2.backward() + adam2 = AdamOptimizer(learning_rate=1e-3) + adam2.minimize(loss2) + model2.clear_gradients() + + self.assertEqual(static_loss, loss.numpy()) + self.assertTrue(np.allclose(static_weight, model.gc.weight.numpy())) + self.assertEqual(static_loss, loss2.numpy()) + self.assertTrue(np.allclose(static_weight, model2.gc.weight.numpy())) + sys.stderr.write('%s %s\n' % (static_loss, loss.numpy())) if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/unittests/test_imperative_mnist.py b/python/paddle/fluid/tests/unittests/test_imperative_mnist.py index 908237b88736da112b7001708bbca19b534baef1..c3a12addfc8ef6e743a34bcdae9237a994b2d178 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_mnist.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_mnist.py @@ -31,7 +31,6 @@ from test_imperative_base import new_program_scope class SimpleImgConvPool(fluid.dygraph.Layer): def __init__(self, name_scope, - num_channels, num_filters, filter_size, pool_size, @@ -51,7 +50,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer): self._conv2d = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, @@ -82,10 +80,10 @@ class MNIST(fluid.dygraph.Layer): super(MNIST, self).__init__(name_scope) self._simple_img_conv_pool_1 = SimpleImgConvPool( - self.full_name(), 1, 20, 5, 2, 2, act="relu") + self.full_name(), 20, 5, 2, 2, act="relu") self._simple_img_conv_pool_2 = SimpleImgConvPool( - self.full_name(), 20, 50, 5, 2, 2, act="relu") + self.full_name(), 50, 5, 2, 2, act="relu") pool_2_shape = 50 * 4 * 4 SIZE = 10 @@ -105,30 +103,45 @@ class MNIST(fluid.dygraph.Layer): class TestImperativeMnist(unittest.TestCase): + def reader_decorator(self, reader): + def _reader_imple(): + for item in reader(): + image = np.array(item[0]).reshape(1, 28, 28) + label = np.array(item[1]).astype('int64').reshape(1) + yield image, label + + return _reader_imple + def test_mnist_float32(self): seed = 90 epoch_num = 1 + batch_size = 128 + batch_num = 50 + with fluid.dygraph.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed mnist = MNIST("mnist") sgd = SGDOptimizer(learning_rate=1e-3) - train_reader = paddle.batch( - paddle.dataset.mnist.train(), batch_size=128, drop_last=True) + + batch_py_reader = fluid.io.PyReader(capacity=1) + batch_py_reader.decorate_sample_list_generator( + paddle.batch( + self.reader_decorator(paddle.dataset.mnist.train()), + batch_size=batch_size, + drop_last=True), + places=fluid.CPUPlace()) mnist.train() dy_param_init_value = {} for epoch in range(epoch_num): - for batch_id, data in enumerate(train_reader()): - dy_x_data = np.array( - [x[0].reshape(1, 28, 28) - for x in data]).astype('float32') - y_data = np.array( - [x[1] for x in data]).astype('int64').reshape(128, 1) - - img = to_variable(dy_x_data) - label = to_variable(y_data) + for batch_id, data in enumerate(batch_py_reader()): + if batch_id >= batch_num: + break + img = data[0] + dy_x_data = img.numpy() + label = data[1] label.stop_gradient = True cost = mnist(img) @@ -159,7 +172,9 @@ class TestImperativeMnist(unittest.TestCase): mnist = MNIST("mnist") sgd = SGDOptimizer(learning_rate=1e-3) train_reader = paddle.batch( - paddle.dataset.mnist.train(), batch_size=128, drop_last=True) + paddle.dataset.mnist.train(), + batch_size=batch_size, + drop_last=True) img = fluid.layers.data( name='pixel', shape=[1, 28, 28], dtype='float32') @@ -183,11 +198,14 @@ class TestImperativeMnist(unittest.TestCase): for epoch in range(epoch_num): for batch_id, data in enumerate(train_reader()): + if batch_id >= batch_num: + break static_x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array( - [x[1] for x in data]).astype('int64').reshape([128, 1]) + [x[1] for x in data]).astype('int64').reshape( + [batch_size, 1]) fetch_list = [avg_loss.name] fetch_list.extend(static_param_name_list) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py index b9f93119e83159c5bc3052b0292168a9ef641d3e..a7c39f7ff2ad8e9dedc99bb37fc0f997853da572 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py @@ -48,29 +48,41 @@ class TestImperativeOptimizerBase(unittest.TestCase): def get_optimizer(self): raise NotImplementedError() + def reader_decorator(self, reader): + def _reader_imple(): + for item in reader(): + image = np.array(item[0]).reshape(1, 28, 28) + label = np.array(item[1]).astype('int64').reshape(1) + yield image, label + + return _reader_imple + def _check_mlp(self): seed = 90 + batch_size = 128 + with fluid.dygraph.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed mlp = MLP('mlp') optimizer = self.get_optimizer() - train_reader = paddle.batch( - paddle.dataset.mnist.train(), batch_size=128, drop_last=True) + + batch_py_reader = fluid.io.PyReader(capacity=1) + batch_py_reader.decorate_sample_list_generator( + paddle.batch( + self.reader_decorator(paddle.dataset.mnist.train()), + batch_size=batch_size, + drop_last=True), + places=fluid.CPUPlace()) dy_param_init_value = {} - for batch_id, data in enumerate(train_reader()): + for batch_id, data in enumerate(batch_py_reader()): if batch_id >= self.batch_num: break - dy_x_data = np.array( - [x[0].reshape(1, 28, 28) for x in data]).astype('float32') - y_data = np.array([x[1] for x in data]).astype('int64').reshape( - 128, 1) - - img = to_variable(dy_x_data) - label = to_variable(y_data) + img = data[0] + label = data[1] label._stop_gradient = True cost = mlp(img) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py b/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py index 088d36be2327a91da0efc639d7f970ed9e43d151..5f6c5b1cb6a5a641b23dbbd82b98c78313efb1ca 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py @@ -44,7 +44,7 @@ class SimpleLSTMRNN(fluid.Layer): self.cell_array = [] self.hidden_array = [] - def build_once(self, input_embedding, init_hidden=None, init_cell=None): + def _build_once(self, input_embedding, init_hidden=None, init_cell=None): self.weight_1_arr = [] self.weight_2_arr = [] self.bias_arr = [] @@ -176,9 +176,6 @@ class PtbModel(fluid.Layer): default_initializer=fluid.initializer.UniformInitializer( low=-self.init_scale, high=self.init_scale)) - def build_once(self, input, label, init_hidden, init_cell): - pass - def forward(self, input, label, init_hidden, init_cell): init_h = fluid.layers.reshape( init_hidden, shape=[self.num_layers, -1, self.hidden_size]) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_resnet.py b/python/paddle/fluid/tests/unittests/test_imperative_resnet.py index d9ef08b3c491b24323bb1469165ed5482737013a..9eab5abc06c96423a99855910009d85cab089f89 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_resnet.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_resnet.py @@ -71,7 +71,6 @@ def optimizer_setting(params): class ConvBNLayer(fluid.Layer): def __init__(self, name_scope, - num_channels, num_filters, filter_size, stride=1, @@ -81,7 +80,6 @@ class ConvBNLayer(fluid.Layer): self._conv = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, @@ -100,30 +98,22 @@ class ConvBNLayer(fluid.Layer): class BottleneckBlock(fluid.Layer): - def __init__(self, - name_scope, - num_channels, - num_filters, - stride, - shortcut=True): + def __init__(self, name_scope, num_filters, stride, shortcut=True): super(BottleneckBlock, self).__init__(name_scope) self.conv0 = ConvBNLayer( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=1, act='relu') self.conv1 = ConvBNLayer( self.full_name(), - num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, act='relu') self.conv2 = ConvBNLayer( self.full_name(), - num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act=None) @@ -131,15 +121,12 @@ class BottleneckBlock(fluid.Layer): if not shortcut: self.short = ConvBNLayer( self.full_name(), - num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, stride=stride) self.shortcut = shortcut - self._num_channels_out = num_filters * 4 - def forward(self, inputs): y = self.conv0(inputs) conv1 = self.conv1(y) @@ -175,7 +162,6 @@ class ResNet(fluid.Layer): self.conv = ConvBNLayer( self.full_name(), - num_channels=3, num_filters=64, filter_size=7, stride=2, @@ -188,7 +174,6 @@ class ResNet(fluid.Layer): pool_type='max') self.bottleneck_block_list = [] - num_channels = 64 for block in range(len(depth)): shortcut = False for i in range(depth[block]): @@ -196,11 +181,9 @@ class ResNet(fluid.Layer): 'bb_%d_%d' % (block, i), BottleneckBlock( self.full_name(), - num_channels=num_channels, num_filters=num_filters[block], stride=2 if i == 0 and block != 0 else 1, shortcut=shortcut)) - num_channels = bottleneck_block._num_channels_out self.bottleneck_block_list.append(bottleneck_block) shortcut = True @@ -227,11 +210,21 @@ class ResNet(fluid.Layer): class TestDygraphResnet(unittest.TestCase): + def reader_decorator(self, reader): + def _reader_imple(): + for item in reader(): + doc = np.array(item[0]).reshape(3, 224, 224) + label = np.array(item[1]).astype('int64').reshape(1) + yield doc, label + + return _reader_imple + def test_resnet_float32(self): seed = 90 batch_size = train_parameters["batch_size"] - batch_num = 20 + batch_num = 10 + with fluid.dygraph.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed @@ -241,25 +234,26 @@ class TestDygraphResnet(unittest.TestCase): np.random.seed(seed) import random random.seed = seed - train_reader = paddle.batch( - paddle.dataset.flowers.train(use_xmap=False), - batch_size=batch_size) + + batch_py_reader = fluid.io.PyReader(capacity=1) + batch_py_reader.decorate_sample_list_generator( + paddle.batch( + self.reader_decorator( + paddle.dataset.flowers.train(use_xmap=False)), + batch_size=batch_size, + drop_last=True), + places=fluid.CPUPlace()) dy_param_init_value = {} for param in resnet.parameters(): dy_param_init_value[param.name] = param.numpy() - for batch_id, data in enumerate(train_reader()): + for batch_id, data in enumerate(batch_py_reader()): if batch_id >= batch_num: break - dy_x_data = np.array( - [x[0].reshape(3, 224, 224) for x in data]).astype('float32') - y_data = np.array([x[1] for x in data]).astype('int64').reshape( - batch_size, 1) - - img = to_variable(dy_x_data) - label = to_variable(y_data) + img = data[0] + label = data[1] label.stop_gradient = True out = resnet(img) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py b/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py index 3f3f92cde57c80fa4ba3d2f1389cc47efd74ca5b..f6585d1b30dacc5a54e38455e8db82980057f1a0 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py @@ -64,7 +64,6 @@ def optimizer_setting(params): class ConvBNLayer(fluid.dygraph.Layer): def __init__(self, name_scope, - num_channels, num_filters, filter_size, stride=1, @@ -74,7 +73,6 @@ class ConvBNLayer(fluid.dygraph.Layer): self._conv = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, @@ -131,20 +129,15 @@ class BottleneckBlock(fluid.dygraph.Layer): super(BottleneckBlock, self).__init__(name_scope) self.conv0 = ConvBNLayer( - self.full_name(), - num_channels=num_channels, - num_filters=num_filters, - filter_size=1) + self.full_name(), num_filters=num_filters, filter_size=1) self.conv1 = ConvBNLayer( self.full_name(), - num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, groups=cardinality) self.conv2 = ConvBNLayer( self.full_name(), - num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act='relu') @@ -157,7 +150,6 @@ class BottleneckBlock(fluid.dygraph.Layer): if not shortcut: self.short = ConvBNLayer( self.full_name(), - num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, stride=stride) @@ -200,7 +192,6 @@ class SeResNeXt(fluid.dygraph.Layer): num_filters = [128, 256, 512, 1024] self.conv0 = ConvBNLayer( self.full_name(), - num_channels=3, num_filters=64, filter_size=7, stride=2, @@ -218,7 +209,6 @@ class SeResNeXt(fluid.dygraph.Layer): num_filters = [128, 256, 512, 1024] self.conv0 = ConvBNLayer( self.full_name(), - num_channels=3, num_filters=3, filter_size=7, stride=2, @@ -236,21 +226,18 @@ class SeResNeXt(fluid.dygraph.Layer): num_filters = [128, 256, 512, 1024] self.conv0 = ConvBNLayer( self.full_name(), - num_channels=3, num_filters=3, filter_size=7, stride=2, act='relu') self.conv1 = ConvBNLayer( self.full_name(), - num_channels=64, num_filters=3, filter_size=7, stride=2, act='relu') self.conv2 = ConvBNLayer( self.full_name(), - num_channels=64, num_filters=3, filter_size=7, stride=2, @@ -311,11 +298,20 @@ class SeResNeXt(fluid.dygraph.Layer): class TestImperativeResneXt(unittest.TestCase): + def reader_decorator(self, reader): + def _reader_imple(): + for item in reader(): + doc = np.array(item[0]).reshape(3, 224, 224) + label = np.array(item[1]).astype('int64').reshape(1) + yield doc, label + + return _reader_imple + def test_se_resnext_float32(self): seed = 90 batch_size = train_parameters["batch_size"] - batch_num = 2 + batch_num = 1 epoch_num = 1 with fluid.dygraph.guard(): fluid.default_startup_program().random_seed = seed @@ -326,29 +322,28 @@ class TestImperativeResneXt(unittest.TestCase): np.random.seed(seed) import random random.seed = seed - train_reader = paddle.batch( - paddle.dataset.flowers.train(use_xmap=False), - batch_size=batch_size, - drop_last=True) + + batch_py_reader = fluid.io.PyReader(capacity=1) + batch_py_reader.decorate_sample_list_generator( + paddle.batch( + self.reader_decorator( + paddle.dataset.flowers.train(use_xmap=False)), + batch_size=batch_size, + drop_last=True), + places=fluid.CPUPlace()) dy_param_init_value = {} for param in se_resnext.parameters(): dy_param_init_value[param.name] = param.numpy() for epoch_id in range(epoch_num): - for batch_id, data in enumerate(train_reader()): + for batch_id, data in enumerate(batch_py_reader()): if batch_id >= batch_num and batch_num != -1: break - dy_x_data = np.array( - [x[0].reshape(3, 224, 224) - for x in data]).astype('float32') - y_data = np.array( - [x[1] for x in data]).astype('int64').reshape( - batch_size, 1) - - img = to_variable(dy_x_data) - label = to_variable(y_data) + img = data[0] + label = data[1] + label.stop_gradient = True label.stop_gradient = True out = se_resnext(img) diff --git a/python/paddle/fluid/tests/unittests/test_infer_shape.py b/python/paddle/fluid/tests/unittests/test_infer_shape.py index 9d5e064e6adabe09094350db2976f83d835520eb..553ebaec7f1bc69ce1bbecd6c88283d6207c179c 100644 --- a/python/paddle/fluid/tests/unittests/test_infer_shape.py +++ b/python/paddle/fluid/tests/unittests/test_infer_shape.py @@ -104,6 +104,7 @@ class TestInferShape(unittest.TestCase): sum_op_desc = block.append_op() sum_op_desc.set_type("expand") sum_op_desc.set_input("X", ["x"]) + sum_op_desc.set_input('expand_times_tensor', []) sum_op_desc.set_output("Out", ["out"]) sum_op_desc._set_attr('expand_times', expand_times) diff --git a/python/paddle/fluid/tests/unittests/test_install_check.py b/python/paddle/fluid/tests/unittests/test_install_check.py index 5802e2ed0a3dfd7e1c45e91037a6c40b1b6bd2fc..5cb199d4967a49f11d656818676bfe855a957bda 100644 --- a/python/paddle/fluid/tests/unittests/test_install_check.py +++ b/python/paddle/fluid/tests/unittests/test_install_check.py @@ -20,3 +20,7 @@ import paddle.fluid as fluid class TestInstallCheck(unittest.TestCase): def test_install_check(self): fluid.install_check.run_check() + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_layer_norm_op.py b/python/paddle/fluid/tests/unittests/test_layer_norm_op.py index fb6c43136ff82af55d1fcc2969cf4a07ae081204..fdc5d3679e71036cf1e1d813e654815eb03dd45c 100644 --- a/python/paddle/fluid/tests/unittests/test_layer_norm_op.py +++ b/python/paddle/fluid/tests/unittests/test_layer_norm_op.py @@ -72,6 +72,9 @@ def _reference_layer_norm_grad(x, grad_y, scale, mean, var, begin_norm_axis=1): class TestLayerNormdOp(unittest.TestCase): + def setUp(self): + self.use_cudnn = True + def __assert_close(self, tensor, np_array, msg, atol=1e-4): self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg) @@ -160,7 +163,8 @@ class TestLayerNormdOp(unittest.TestCase): self.__assert_close(bias_grad, out[5], "bias_grad") places = [core.CPUPlace()] - if core.is_compiled_with_cuda() and core.op_support_gpu("layer_norm"): + if core.is_compiled_with_cuda() and core.op_support_gpu( + "layer_norm") and self.use_cudnn: places.append(core.CUDAPlace(0)) for place in places: diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py index 2474125835fbf54316e26d272eec940fc380a448..944b1bb12fe20486777972caffc4d69faebb5bea 100644 --- a/python/paddle/fluid/tests/unittests/test_layers.py +++ b/python/paddle/fluid/tests/unittests/test_layers.py @@ -190,8 +190,7 @@ class TestLayer(LayerTest): with self.static_graph(): images = layers.data(name='pixel', shape=[3, 5, 5], dtype='float32') - conv2d = nn.Conv2D( - 'conv2d', num_channels=3, num_filters=3, filter_size=[2, 2]) + conv2d = nn.Conv2D('conv2d', num_filters=3, filter_size=[2, 2]) ret = conv2d(images) static_ret2 = self.get_static_graph_result( feed={'pixel': np.ones( @@ -200,8 +199,7 @@ class TestLayer(LayerTest): with self.dynamic_graph(): images = np.ones([2, 3, 5, 5], dtype='float32') - conv2d = nn.Conv2D( - 'conv2d', num_channels=3, num_filters=3, filter_size=[2, 2]) + conv2d = nn.Conv2D('conv2d', num_filters=3, filter_size=[2, 2]) dy_ret = conv2d(base.to_variable(images)) self.assertTrue(np.allclose(static_ret, dy_ret.numpy())) @@ -1267,6 +1265,12 @@ class TestBook(LayerTest): out = layers.scatter(input=x, index=idx, updates=updates) return (out) + def make_one_hot(self): + with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()): + label = self._get_data(name="label", shape=[1], dtype="int32") + one_hot_label = layers.one_hot(input=label, depth=10) + return (one_hot_label) + def make_label_smooth(self): # TODO(minqiyang): support gpu ut self._force_to_use_cpu = True @@ -1957,6 +1961,173 @@ class TestBook(LayerTest): self.assertIsNotNone(out) print(str(program)) + def test_deformable_conv(self): + if core.is_compiled_with_cuda(): + with program_guard(fluid.default_main_program(), + fluid.default_startup_program()): + input = layers.data( + name='input', + append_batch_size=False, + shape=[2, 3, 32, 32], + dtype="float32") + offset = layers.data( + name='offset', + append_batch_size=False, + shape=[2, 18, 32, 32], + dtype="float32") + mask = layers.data( + name='mask', + append_batch_size=False, + shape=[2, 9, 32, 32], + dtype="float32") + out = layers.deformable_conv( + input=input, + offset=offset, + mask=mask, + num_filters=2, + filter_size=3, + padding=1) + return (out) + + def test_unfold(self): + with self.static_graph(): + x = layers.data(name='x', shape=[3, 20, 20], dtype='float32') + out = layers.unfold(x, [3, 3], 1, 1, 1) + return (out) + + def test_deform_roi_pooling(self): + with program_guard(fluid.default_main_program(), + fluid.default_startup_program()): + input = layers.data( + name='input', + shape=[2, 3, 32, 32], + dtype='float32', + append_batch_size=False) + rois = layers.data( + name="rois", shape=[4], dtype='float32', lod_level=1) + trans = layers.data( + name="trans", + shape=[2, 3, 32, 32], + dtype='float32', + append_batch_size=False) + out = layers.deformable_roi_pooling( + input=input, + rois=rois, + trans=trans, + no_trans=False, + spatial_scale=1.0, + group_size=(1, 1), + pooled_height=8, + pooled_width=8, + part_size=(8, 8), + sample_per_part=4, + trans_std=0.1) + return (out) + + def test_retinanet_target_assign(self): + with program_guard(fluid.default_main_program(), + fluid.default_startup_program()): + bbox_pred = layers.data( + name='bbox_pred', + shape=[1, 100, 4], + append_batch_size=False, + dtype='float32') + cls_logits = layers.data( + name='cls_logits', + shape=[1, 100, 10], + append_batch_size=False, + dtype='float32') + anchor_box = layers.data( + name='anchor_box', + shape=[100, 4], + append_batch_size=False, + dtype='float32') + anchor_var = layers.data( + name='anchor_var', + shape=[100, 4], + append_batch_size=False, + dtype='float32') + gt_boxes = layers.data( + name='gt_boxes', + shape=[10, 4], + append_batch_size=False, + dtype='float32') + gt_labels = layers.data( + name='gt_labels', + shape=[10, 1], + append_batch_size=False, + dtype='float32') + is_crowd = layers.data( + name='is_crowd', + shape=[1], + append_batch_size=False, + dtype='float32') + im_info = layers.data( + name='im_info', + shape=[1, 3], + append_batch_size=False, + dtype='float32') + return (layers.retinanet_target_assign( + bbox_pred, cls_logits, anchor_box, anchor_var, gt_boxes, + gt_labels, is_crowd, im_info, 10)) + + def test_sigmoid_focal_loss(self): + with program_guard(fluid.default_main_program(), + fluid.default_startup_program()): + input = layers.data( + name='data', + shape=[10, 80], + append_batch_size=False, + dtype='float32') + label = layers.data( + name='label', + shape=[10, 1], + append_batch_size=False, + dtype='int32') + fg_num = layers.data( + name='fg_num', + shape=[1], + append_batch_size=False, + dtype='int32') + out = fluid.layers.sigmoid_focal_loss( + x=input, label=label, fg_num=fg_num, gamma=2., alpha=0.25) + return (out) + + def test_retinanet_detection_output(self): + with program_guard(fluid.default_main_program(), + fluid.default_startup_program()): + bboxes = layers.data( + name='bboxes', + shape=[1, 21, 4], + append_batch_size=False, + dtype='float32') + scores = layers.data( + name='scores', + shape=[1, 21, 10], + append_batch_size=False, + dtype='float32') + anchors = layers.data( + name='anchors', + shape=[21, 4], + append_batch_size=False, + dtype='float32') + im_info = layers.data( + name="im_info", + shape=[1, 3], + append_batch_size=False, + dtype='float32') + nmsed_outs = layers.retinanet_detection_output( + bboxes=[bboxes, bboxes], + scores=[scores, scores], + anchors=[anchors, anchors], + im_info=im_info, + score_threshold=0.05, + nms_top_k=1000, + keep_top_k=100, + nms_threshold=0.3, + nms_eta=1.) + return (nmsed_outs) + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_nn_grad.py b/python/paddle/fluid/tests/unittests/test_nn_grad.py index df0d8e0345cf497f264b59049a4e01ba9aa85d85..ae1e85c483e66b2397ff7aef2baaa25182e87c83 100644 --- a/python/paddle/fluid/tests/unittests/test_nn_grad.py +++ b/python/paddle/fluid/tests/unittests/test_nn_grad.py @@ -43,44 +43,41 @@ class TestMulGradCheck(unittest.TestCase): self.func(p) -class TestReluDoubleGradCheck(unittest.TestCase): +class TestConvDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): - shape = [2, 8] + shape = [2, 4, 14, 16] eps = 0.005 dtype = np.float64 - x = layers.data('x', shape, False, dtype) - x.persistable = True - y = layers.relu(x) + y = layers.conv2d(x, 4, 1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) - x_arr[np.abs(x_arr) < 0.005] = 0.02 + w = fluid.default_main_program().global_block().all_parameters() + w_arr = [] + for p in w: + w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( - [x], y, x_init=x_arr, place=place, eps=eps) + [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): - places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): - places.append(fluid.CUDAPlace(0)) - for p in places: - self.func(p) + places = [fluid.CUDAPlace(0)] + for p in places: + self.func(p) -class TestLeakyReluDoubleGradCheck(unittest.TestCase): +class TestReduceMeanWithDimDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): - shape = [3, 7] - eps = 0.005 - alpha = 0.2 + shape = [7, 11] + eps = 0.05 dtype = np.float64 x = layers.data('x', shape, False, dtype) x.persistable = True - - y = layers.leaky_relu(x, alpha=alpha) + y = layers.reduce_mean(x, dim=0) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) - x_arr[np.abs(x_arr) < 0.005] = 0.02 gradient_checker.double_grad_check( [x], y, x_init=x_arr, place=place, eps=eps) @@ -89,30 +86,36 @@ class TestLeakyReluDoubleGradCheck(unittest.TestCase): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) + for p in places: + self.func(p) -class TestConvDoubleGradCheck(unittest.TestCase): +class TestMulDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): - shape = [2, 4, 14, 16] + # the shape of input variable shoule be clearly specified, not inlcude -1. + x_shape = [7, 11] + y_shape = [11, 9] eps = 0.005 dtype = np.float64 - x = layers.data('x', shape, False, dtype) - y = layers.conv2d(x, 4, 1, bias_attr=False) - x_arr = np.random.uniform(-1, 1, shape).astype(dtype) - w = fluid.default_main_program().global_block().all_parameters() - w_arr = [] - for p in w: - w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) + x = layers.data('x', x_shape, False, dtype) + x.persistable = True + y = layers.data('y', y_shape, False, dtype) + y.persistable = True + out = layers.mul(x, y) + x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) + y_arr = np.random.uniform(-1, 1, y_shape).astype(dtype) + gradient_checker.double_grad_check( - [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) + [x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps) def test_grad(self): + places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): - places = [fluid.CUDAPlace(0)] - for p in places: - self.func(p) + places.append(fluid.CUDAPlace(0)) + for p in places: + self.func(p) if __name__ == "__main__": diff --git a/python/paddle/fluid/tests/unittests/test_one_hot_op.py b/python/paddle/fluid/tests/unittests/test_one_hot_op.py index 7afdae804a65b9fb05a521a1b08ce0bfb21d721f..f213a0c77f4babdb46626c6e7d9b631a4e79a631 100644 --- a/python/paddle/fluid/tests/unittests/test_one_hot_op.py +++ b/python/paddle/fluid/tests/unittests/test_one_hot_op.py @@ -28,10 +28,34 @@ class TestOneHotOp(OpTest): def setUp(self): self.op_type = 'one_hot' depth = 10 + depth_np = np.array(10).astype('int32') dimension = 12 x_lod = [[4, 1, 3, 3]] x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))] - x = np.array(x).astype('int').reshape([sum(x_lod[0]), 1]) + x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1]) + + out = np.zeros(shape=(np.product(x.shape[:-1]), + depth)).astype('float32') + + for i in range(np.product(x.shape)): + out[i, x[i]] = 1.0 + + self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np} + self.attrs = {'dtype': int(core.VarDesc.VarType.FP32)} + self.outputs = {'Out': (out, x_lod)} + + def test_check_output(self): + self.check_output() + + +class TestOneHotOp_attr(OpTest): + def setUp(self): + self.op_type = 'one_hot' + depth = 10 + dimension = 12 + x_lod = [[4, 1, 3, 3]] + x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))] + x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1]) out = np.zeros(shape=(np.product(x.shape[:-1]), depth)).astype('float32') @@ -40,7 +64,7 @@ class TestOneHotOp(OpTest): out[i, x[i]] = 1.0 self.inputs = {'X': (x, x_lod)} - self.attrs = {'depth': depth, 'dtype': int(core.VarDesc.VarType.FP32)} + self.attrs = {'dtype': int(core.VarDesc.VarType.FP32), 'depth': depth} self.outputs = {'Out': (out, x_lod)} def test_check_output(self): @@ -48,13 +72,37 @@ class TestOneHotOp(OpTest): class TestOneHotOp_default_dtype(OpTest): + def setUp(self): + self.op_type = 'one_hot' + depth = 10 + depth_np = np.array(10).astype('int32') + dimension = 12 + x_lod = [[4, 1, 3, 3]] + x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))] + x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1]) + + out = np.zeros(shape=(np.product(x.shape[:-1]), + depth)).astype('float32') + + for i in range(np.product(x.shape)): + out[i, x[i]] = 1.0 + + self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np} + self.attrs = {} + self.outputs = {'Out': (out, x_lod)} + + def test_check_output(self): + self.check_output() + + +class TestOneHotOp_default_dtype_attr(OpTest): def setUp(self): self.op_type = 'one_hot' depth = 10 dimension = 12 x_lod = [[4, 1, 3, 3]] x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))] - x = np.array(x).astype('int').reshape([sum(x_lod[0]), 1]) + x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1]) out = np.zeros(shape=(np.product(x.shape[:-1]), depth)).astype('float32') diff --git a/python/paddle/fluid/tests/unittests/test_operator_desc.py b/python/paddle/fluid/tests/unittests/test_operator_desc.py index 37b9a9188ab44df81029ae6d9925ae21c1929cff..aa9634a2d419cbe791b42af526fe2e2bc37a5727 100644 --- a/python/paddle/fluid/tests/unittests/test_operator_desc.py +++ b/python/paddle/fluid/tests/unittests/test_operator_desc.py @@ -33,7 +33,7 @@ class TestOperator(unittest.TestCase): except ValueError as v_err: self.assertEqual( cpt.get_exception_message(v_err), - "`type` to initilized an Operator can not be None.") + "`type` to initialized an Operator can not be None.") try: block.append_op(type="no_such_op") self.assertFail() diff --git a/python/paddle/fluid/tests/unittests/test_parallel_dygraph_mnist.py b/python/paddle/fluid/tests/unittests/test_parallel_dygraph_mnist.py index a08991986a7ccbfc446d4dcab9a88b926ef6eea8..ecdca39a543204b4ab3c1918a8f83acf2e538ae2 100644 --- a/python/paddle/fluid/tests/unittests/test_parallel_dygraph_mnist.py +++ b/python/paddle/fluid/tests/unittests/test_parallel_dygraph_mnist.py @@ -13,10 +13,13 @@ # limitations under the License. from __future__ import print_function -import unittest +#import unittest from test_dist_base import TestDistBase +import paddle.fluid as fluid - +#TODO(guru4elephant): should have dygraph test dist base +# current TestDistBase has some incompatible code with dygraph +''' class TestParallelDygraphMnist(TestDistBase): def _setup_config(self): self._sync_mode = False @@ -24,9 +27,11 @@ class TestParallelDygraphMnist(TestDistBase): self._dygraph = True def test_mnist(self): - self.check_with_place( - "parallel_dygraph_mnist.py", delta=1e-5, check_error_log=True) - + return + if fluid.core.is_compiled_with_cuda(): + self.check_with_place("parallel_dygraph_mnist.py", delta=1e-5) +''' if __name__ == "__main__": - unittest.main() + #unittest.main() + pass diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor_dry_run.py b/python/paddle/fluid/tests/unittests/test_parallel_executor_dry_run.py index d0eca7d6dfbdf03828125508c798a9bd31f8bbd6..328b3a4813eec261d39985ef80c47d0c827380ca 100644 --- a/python/paddle/fluid/tests/unittests/test_parallel_executor_dry_run.py +++ b/python/paddle/fluid/tests/unittests/test_parallel_executor_dry_run.py @@ -17,6 +17,8 @@ from paddle.fluid import compiler import unittest import logging import six +import os +os.environ['CPU_NUM'] = str(4) class TestBase(unittest.TestCase): diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py b/python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py index 645b0188d5f45935ace074ba343de246af476b41..0457e9cefdb391eb3bdb713f8a35bed769b9bce8 100644 --- a/python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py +++ b/python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py @@ -45,7 +45,8 @@ class TestFetchAndFeed(unittest.TestCase): def parallel_exe(self, use_cuda, run_parallel_exe, - use_experimental_executor=False, + use_faster_executor=False, + num_threads=4, seed=1): main_program = fluid.Program() startup = fluid.Program() @@ -72,7 +73,8 @@ class TestFetchAndFeed(unittest.TestCase): build_strategy.enable_inplace = False build_strategy.memory_optimize = False exec_strategy = fluid.ExecutionStrategy() - exec_strategy.use_experimental_executor = use_experimental_executor + exec_strategy.use_experimental_executor = use_faster_executor + exec_strategy.num_threads = num_threads train_cp = compiler.CompiledProgram(main_program).with_data_parallel( loss_name=loss.name, build_strategy=build_strategy, @@ -143,24 +145,25 @@ class TestFetchAndFeed(unittest.TestCase): if batch_id == 2: break - def test_fetch_with_threaded_executor(self): - if core.is_compiled_with_cuda(): - self.parallel_exe( - use_cuda=True, - run_parallel_exe=self.run_parallel_exe_with_fetch) - self.parallel_exe( - use_cuda=False, run_parallel_exe=self.run_parallel_exe_with_fetch) - - def test_fetch_with_fast_threaded_executor(self): + def check_executor(self, use_faster_executor=False, num_threads=4): if core.is_compiled_with_cuda(): self.parallel_exe( use_cuda=True, run_parallel_exe=self.run_parallel_exe_with_fetch, - use_experimental_executor=True) + use_faster_executor=use_faster_executor, + num_threads=num_threads) self.parallel_exe( use_cuda=False, run_parallel_exe=self.run_parallel_exe_with_fetch, - use_experimental_executor=True) + use_faster_executor=use_faster_executor, + num_threads=num_threads) + + def test_fetch(self): + for use_faster_executor in {True, False}: + self.check_executor( + use_faster_executor=use_faster_executor, num_threads=4) + self.check_executor( + use_faster_executor=use_faster_executor, num_threads=1) def test_feed(self): if core.is_compiled_with_cuda(): diff --git a/python/paddle/fluid/tests/unittests/test_print_op.py b/python/paddle/fluid/tests/unittests/test_print_op.py index 8097b5f734343ca97c131474338ed1cd60eefc85..0fc11ef8d9220dcc6875b6df2a3e527244872e11 100644 --- a/python/paddle/fluid/tests/unittests/test_print_op.py +++ b/python/paddle/fluid/tests/unittests/test_print_op.py @@ -17,11 +17,13 @@ from __future__ import print_function import unittest import paddle.fluid.core as core from paddle.fluid.executor import Executor +import paddle.fluid as fluid import paddle.fluid.layers as layers from paddle.fluid.backward import append_backward from paddle.fluid.framework import switch_main_program from paddle.fluid.framework import Program import numpy as np +from simple_nets import simple_fc_net, init_data class TestPrintOpCPU(unittest.TestCase): @@ -56,6 +58,27 @@ class TestPrintOpCPU(unittest.TestCase): fetch_list=[loss], return_numpy=False) + def test_all_parameters(self): + x = layers.data('x', shape=[3], dtype='float32', lod_level=1) + x.stop_gradient = False + + for print_tensor_name in [True, False]: + for print_tensor_type in [True, False]: + for print_tensor_shape in [True, False]: + for print_tensor_lod in [True, False]: + layers.Print( + input=x, + print_tensor_name=print_tensor_name, + print_tensor_type=print_tensor_type, + print_tensor_shape=print_tensor_shape, + print_tensor_lod=print_tensor_lod, ) + loss = layers.mean(x) + append_backward(loss=loss) + exe = Executor(self.place) + outs = exe.run(feed={'x': self.x_tensor}, + fetch_list=[loss], + return_numpy=False) + @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") @@ -68,5 +91,35 @@ class TestPrintOpGPU(TestPrintOpCPU): self.x_tensor.set_recursive_sequence_lengths([[1, 1]]) +class TestPrintOpBackward(unittest.TestCase): + def check_backward(self, use_cuda): + main = fluid.Program() + startup = fluid.Program() + + with fluid.program_guard(main, startup): + loss = simple_fc_net() + loss = fluid.layers.Print(loss) + fluid.optimizer.Adam().minimize(loss) + + print_ops = [op for op in main.blocks[0].ops if op.type == u'print'] + assert len(print_ops) == 2, "The number of print op should be 2" + + place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() + exe = fluid.Executor(place) + exe.run(startup) + + binary = fluid.compiler.CompiledProgram(main).with_data_parallel( + loss_name=loss.name) + + img, label = init_data() + feed_dict = {"image": img, "label": label} + exe.run(binary, feed_dict) + + def test_fw_bw(self): + if core.is_compiled_with_cuda(): + self.check_backward(use_cuda=True) + self.check_backward(use_cuda=False) + + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_py_reader_using_executor.py b/python/paddle/fluid/tests/unittests/test_py_reader_using_executor.py index a3701f0808b98b80b62866ffe1250d065361025c..e4fb9b1970a8da4bfec5d48f1182e9552aa77ca8 100644 --- a/python/paddle/fluid/tests/unittests/test_py_reader_using_executor.py +++ b/python/paddle/fluid/tests/unittests/test_py_reader_using_executor.py @@ -22,6 +22,7 @@ import numpy as np import threading import multiprocessing import os +os.environ['CPU_NUM'] = str(4) def as_tensor(np_array_or_tensor, place=None): diff --git a/python/paddle/fluid/tests/unittests/test_recordio_reader.py b/python/paddle/fluid/tests/unittests/test_recordio_reader.py index f5009556adc8951aad80532d77cac4b920887c66..0417da7228e96ed8daffa7bbfcb7c12358cd78ec 100644 --- a/python/paddle/fluid/tests/unittests/test_recordio_reader.py +++ b/python/paddle/fluid/tests/unittests/test_recordio_reader.py @@ -86,3 +86,7 @@ class TestRecordIO(unittest.TestCase): def test_double_buffer_reader(self): self.test_main(decorator_callback=lambda reader: fluid.layers.io.double_buffer(reader, place='cuda:0' if fluid.core.is_compiled_with_cuda() else 'cpu')) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_reshape_op.py b/python/paddle/fluid/tests/unittests/test_reshape_op.py index 7691221a5511ce83176ff07c231873f66ca371ed..3221985c442e5a09db468df616f203400af52371 100644 --- a/python/paddle/fluid/tests/unittests/test_reshape_op.py +++ b/python/paddle/fluid/tests/unittests/test_reshape_op.py @@ -37,6 +37,7 @@ class TestReshapeOp(OpTest): self.infered_shape = (5, 10) def test_check_output(self): + self.check_output(no_check_set=['XShape']) def test_check_grad(self): @@ -82,5 +83,51 @@ class TestReshapeOpWithInputShape(OpTest): self.check_grad(["X"], "Out") +class TestReshapeOp_attr_tensor(OpTest): + def setUp(self): + self.init_data() + self.op_type = "reshape2" + + shape_tensor = [] + for index, ele in enumerate(self.new_shape): + shape_tensor.append(("x" + str(index), np.ones( + (1)).astype('int32') * ele)) + + self.inputs = { + "X": np.random.random(self.ori_shape).astype("float32"), + 'ShapeTensor': shape_tensor + } + self.attrs = {} + self.outputs = { + "Out": self.inputs["X"].reshape(self.infered_shape), + 'XShape': np.random.random(self.ori_shape).astype("float32") + } + + def init_data(self): + self.ori_shape = (2, 25) + self.new_shape = (5, 10) + self.infered_shape = (5, 10) + + def test_check_output(self): + self.check_output(no_check_set=['XShape']) + + def test_check_grad(self): + self.check_grad(["X"], "Out") + + +class TestReshapeOpDimInfer1_attr_tensor(TestReshapeOp_attr_tensor): + def init_data(self): + self.ori_shape = (5, 10) + self.new_shape = (5, -1, 5) + self.infered_shape = (5, -1, 5) + + +class TestReshapeOpDimInfer2_attr_tensor(TestReshapeOp_attr_tensor): + def init_data(self): + self.ori_shape = (2, 2, 6) + self.new_shape = (2, 0, 3, -1) + self.infered_shape = (2, 2, 3, -1) + + if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py b/python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py index 1a2c9bb5f43d55d8e6183de0d55bfcc2b9ac3f08..3dba961dc9df070f8920629e759acf7de6275ee7 100644 --- a/python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py +++ b/python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py @@ -167,6 +167,105 @@ def rpn_target_assign_in_python(all_anchors, return loc_indexes, score_indexes, tgt_bboxes, tgt_labels, bbox_inside_weights +def retinanet_target_assign(anchor_by_gt_overlap, gt_labels, positive_overlap, + negative_overlap): + anchor_to_gt_argmax = anchor_by_gt_overlap.argmax(axis=1) + anchor_to_gt_max = anchor_by_gt_overlap[np.arange( + anchor_by_gt_overlap.shape[0]), anchor_to_gt_argmax] + + gt_to_anchor_argmax = anchor_by_gt_overlap.argmax(axis=0) + gt_to_anchor_max = anchor_by_gt_overlap[gt_to_anchor_argmax, np.arange( + anchor_by_gt_overlap.shape[1])] + anchors_with_max_overlap = np.where( + anchor_by_gt_overlap == gt_to_anchor_max)[0] + + labels = np.ones((anchor_by_gt_overlap.shape[0], ), dtype=np.int32) * -1 + labels[anchors_with_max_overlap] = 1 + labels[anchor_to_gt_max >= positive_overlap] = 1 + + fg_inds = np.where(labels == 1)[0] + bbox_inside_weight = np.zeros((len(fg_inds), 4), dtype=np.float32) + + bg_inds = np.where(anchor_to_gt_max < negative_overlap)[0] + enable_inds = bg_inds + + fg_fake_inds = np.array([], np.int32) + fg_value = np.array([fg_inds[0]], np.int32) + fake_num = 0 + for bg_id in enable_inds: + if bg_id in fg_inds: + fake_num += 1 + fg_fake_inds = np.hstack([fg_fake_inds, fg_value]) + labels[enable_inds] = 0 + + bbox_inside_weight[fake_num:, :] = 1 + fg_inds = np.where(labels == 1)[0] + bg_inds = np.where(labels == 0)[0] + loc_index = np.hstack([fg_fake_inds, fg_inds]) + score_index = np.hstack([fg_inds, bg_inds]) + score_index_tmp = np.hstack([fg_inds]) + labels = labels[score_index] + + gt_inds = anchor_to_gt_argmax[loc_index] + label_inds = anchor_to_gt_argmax[score_index_tmp] + labels[0:len(fg_inds)] = np.squeeze(gt_labels[label_inds]) + fg_num = len(fg_fake_inds) + len(fg_inds) + 1 + assert not np.any(labels == -1), "Wrong labels with -1" + return loc_index, score_index, labels, gt_inds, bbox_inside_weight, fg_num + + +def retinanet_target_assign_in_python(all_anchors, gt_boxes, gt_labels, + is_crowd, im_info, lod, positive_overlap, + negative_overlap): + anchor_num = all_anchors.shape[0] + batch_size = len(lod) - 1 + for i in range(batch_size): + im_scale = im_info[i][2] + + inds_inside = np.arange(all_anchors.shape[0]) + inside_anchors = all_anchors + b, e = lod[i], lod[i + 1] + gt_boxes_slice = gt_boxes[b:e, :] * im_scale + gt_labels_slice = gt_labels[b:e, :] + is_crowd_slice = is_crowd[b:e] + + not_crowd_inds = np.where(is_crowd_slice == 0)[0] + gt_boxes_slice = gt_boxes_slice[not_crowd_inds] + gt_labels_slice = gt_labels_slice[not_crowd_inds] + iou = _bbox_overlaps(inside_anchors, gt_boxes_slice) + + loc_inds, score_inds, labels, gt_inds, bbox_inside_weight, fg_num = \ + retinanet_target_assign(iou, gt_labels_slice, + positive_overlap, negative_overlap) + # unmap to all anchor + loc_inds = inds_inside[loc_inds] + score_inds = inds_inside[score_inds] + + sampled_gt = gt_boxes_slice[gt_inds] + sampled_anchor = all_anchors[loc_inds] + box_deltas = _box_to_delta(sampled_anchor, sampled_gt, [1., 1., 1., 1.]) + + if i == 0: + loc_indexes = loc_inds + score_indexes = score_inds + tgt_labels = labels + tgt_bboxes = box_deltas + bbox_inside_weights = bbox_inside_weight + fg_nums = [[fg_num]] + else: + loc_indexes = np.concatenate( + [loc_indexes, loc_inds + i * anchor_num]) + score_indexes = np.concatenate( + [score_indexes, score_inds + i * anchor_num]) + tgt_labels = np.concatenate([tgt_labels, labels]) + tgt_bboxes = np.vstack([tgt_bboxes, box_deltas]) + bbox_inside_weights = np.vstack([bbox_inside_weights, \ + bbox_inside_weight]) + fg_nums = np.concatenate([fg_nums, [[fg_num]]]) + + return loc_indexes, score_indexes, tgt_bboxes, tgt_labels, bbox_inside_weights, fg_nums + + class TestRpnTargetAssignOp(OpTest): def setUp(self): n, c, h, w = 2, 4, 14, 14 @@ -234,5 +333,65 @@ class TestRpnTargetAssignOp(OpTest): self.check_output() +class TestRetinanetTargetAssignOp(OpTest): + def setUp(self): + n, c, h, w = 2, 4, 14, 14 + all_anchors = get_anchor(n, c, h, w) + gt_num = 10 + all_anchors = all_anchors.reshape(-1, 4) + anchor_num = all_anchors.shape[0] + + images_shape = [[64, 64], [64, 64]] + groundtruth, lod = _generate_groundtruth(images_shape, 3, 4) + lod = [0, 4, 8] + + im_info = np.ones((len(images_shape), 3)).astype(np.float32) + for i in range(len(images_shape)): + im_info[i, 0] = images_shape[i][0] + im_info[i, 1] = images_shape[i][1] + im_info[i, 2] = 0.8 #scale + gt_boxes = np.vstack([v['boxes'] for v in groundtruth]) + is_crowd = np.hstack([v['is_crowd'] for v in groundtruth]) + gt_labels = np.vstack([ + v['gt_classes'].reshape(len(v['gt_classes']), 1) + for v in groundtruth + ]) + gt_labels = gt_labels.reshape(len(gt_labels), 1) + all_anchors = all_anchors.astype('float32') + gt_boxes = gt_boxes.astype('float32') + gt_labels = gt_labels.astype('int32') + + positive_overlap = 0.5 + negative_overlap = 0.4 + + loc_index, score_index, tgt_bbox, labels, bbox_inside_weights, fg_num = \ + retinanet_target_assign_in_python(all_anchors, gt_boxes, gt_labels, is_crowd, + im_info, lod, positive_overlap, negative_overlap) + labels = labels[:, np.newaxis] + self.op_type = "retinanet_target_assign" + self.inputs = { + 'Anchor': all_anchors, + 'GtBoxes': (gt_boxes, [[4, 4]]), + 'GtLabels': (gt_labels, [[4, 4]]), + 'IsCrowd': (is_crowd, [[4, 4]]), + 'ImInfo': (im_info, [[1, 1]]) + } + self.attrs = { + 'positive_overlap': positive_overlap, + 'negative_overlap': negative_overlap + } + self.outputs = { + 'LocationIndex': loc_index.astype('int32'), + 'ScoreIndex': score_index.astype('int32'), + 'TargetBBox': tgt_bbox.astype('float32'), + 'TargetLabel': labels.astype('int32'), + 'BBoxInsideWeight': bbox_inside_weights.astype('float32'), + 'ForegroundNumber': fg_num.astype('int32') + } + + def test_check_output(self): + self.check_output() + + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_scatter_op.py b/python/paddle/fluid/tests/unittests/test_scatter_op.py index 088996f9d7dee1ea914e36e3342c9a5110001c44..9c60a1182852ba1c524f7185a2786c9a8943315f 100644 --- a/python/paddle/fluid/tests/unittests/test_scatter_op.py +++ b/python/paddle/fluid/tests/unittests/test_scatter_op.py @@ -17,6 +17,7 @@ from __future__ import print_function import unittest import numpy as np from op_test import OpTest +import paddle.fluid.core as core class TestScatterOp(OpTest): @@ -37,5 +38,98 @@ class TestScatterOp(OpTest): self.check_grad(['Updates'], 'Out', in_place=True) +class TestScatterOp0(OpTest): + def setUp(self): + self.op_type = "scatter" + ref_np = np.ones((3, 3)).astype("float32") + index_np = np.array([1, 2]).astype("int32") + updates_np = np.random.random((2, 3)).astype("float32") + output_np = np.copy(ref_np) + output_np[index_np] = updates_np + self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np} + self.attrs = {'overwrite': True} + self.outputs = {'Out': output_np} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['Updates'], 'Out', in_place=True) + + +class TestScatterOp1(OpTest): + def setUp(self): + self.op_type = "scatter" + ref_np = np.ones((3, 3)).astype("float32") + zeros_np = np.zeros([2, 3]).astype('float32') + index_np = np.array([1, 1]).astype("int32") + updates_np = np.random.random((2, 3)).astype("float32") + output_np = np.copy(ref_np) + output_np[index_np] = zeros_np + for i in range(0, len(index_np)): + output_np[index_np[i]] += updates_np[i] + self.attrs = {'overwrite': False} + self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np} + self.outputs = {'Out': output_np} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['Updates'], 'Out', in_place=True) + + +@unittest.skipIf(not core.is_compiled_with_cuda(), + "core is not compiled with CUDA") +class TestScatterOp2(OpTest): + def setUp(self): + self.op_type = "scatter" + ref_np = np.ones((3, 3)).astype("float32") + index_np = np.array([1, 2]).astype("int32") + updates_np = np.random.random((2, 3)).astype("float32") + output_np = np.copy(ref_np) + output_np[index_np] = updates_np + self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np} + self.outputs = {'Out': output_np} + + def test_check_output(self): + if core.is_compiled_with_cuda(): + place = core.CUDAPlace(0) + self.check_output_with_place(place, atol=1e-3) + + def test_check_grad(self): + if core.is_compiled_with_cuda(): + place = core.CUDAPlace(0) + self.check_grad_with_place(place, ['Updates'], 'Out', in_place=True) + + +@unittest.skipIf(not core.is_compiled_with_cuda(), + "core is not compiled with CUDA") +class TestScatterOp3(OpTest): + def setUp(self): + self.op_type = "scatter" + ref_np = np.ones((3, 3)).astype("float32") + zeros_np = np.zeros([2, 3]).astype('float32') + index_np = np.array([1, 1]).astype("int32") + updates_np = np.random.random((2, 3)).astype("float32") + output_np = np.copy(ref_np) + output_np[index_np] = zeros_np + for i in range(0, len(index_np)): + output_np[index_np[i]] += updates_np[i] + self.attrs = {'overwrite': False} + self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np} + self.outputs = {'Out': output_np} + + def test_check_output(self): + if core.is_compiled_with_cuda(): + place = core.CUDAPlace(0) + self.check_output_with_place(place, atol=1e-3) + + def test_check_grad(self): + if core.is_compiled_with_cuda(): + place = core.CUDAPlace(0) + self.check_grad_with_place(place, ['Updates'], 'Out', in_place=True) + + if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_seq_pool.py b/python/paddle/fluid/tests/unittests/test_seq_pool.py index 176265428c83c7758eabf86b5b703363b6ee3919..aa801b1f5d8c7e7c8acec7096db7010a058451ff 100644 --- a/python/paddle/fluid/tests/unittests/test_seq_pool.py +++ b/python/paddle/fluid/tests/unittests/test_seq_pool.py @@ -20,31 +20,42 @@ from op_test import OpTest from test_reorder_lod_tensor import convert_to_offset -def compute_seqpool_sum(x, offset, out): +def compute_seqpool_sum(x, offset, out, pad_value=0.0): for i in range(len(offset[0]) - 1): - sub_x = x[offset[0][i]:offset[0][i + 1], :] - out[i] = sub_x.sum(axis=0) + if offset[0][i] == offset[0][i + 1]: + out[i] = pad_value + else: + sub_x = x[offset[0][i]:offset[0][i + 1], :] + out[i] = sub_x.sum(axis=0) -def compute_seqpool_avg(x, offset, out): +def compute_seqpool_avg(x, offset, out, pad_value=0.0): for i in range(len(offset[0]) - 1): - sub_x = x[offset[0][i]:offset[0][i + 1], :] - out[i] = sub_x.mean(axis=0) + if offset[0][i] == offset[0][i + 1]: + out[i] = pad_value + else: + sub_x = x[offset[0][i]:offset[0][i + 1], :] + out[i] = sub_x.mean(axis=0) -def compute_seqpool_sqrt(x, offset, out): +def compute_seqpool_sqrt(x, offset, out, pad_value=0.0): for i in range(len(offset[0]) - 1): - sub_x = x[offset[0][i]:offset[0][i + 1], :] - seq_len = offset[0][i + 1] - offset[0][i] - out[i] = sub_x.sum(axis=0) / np.sqrt(seq_len) + if offset[0][i] == offset[0][i + 1]: + out[i] = pad_value + else: + sub_x = x[offset[0][i]:offset[0][i + 1], :] + seq_len = offset[0][i + 1] - offset[0][i] + out[i] = sub_x.sum(axis=0) / np.sqrt(seq_len) class TestSeqAvgPool(OpTest): + def set_lod(self): + return [[11]] + def set_data(self): self.op_type = 'sequence_pool' - # one level, batch size is 4 x = np.random.uniform(0.1, 1, [11, 23]).astype('float32') - lod = [[11]] + lod = self.set_lod() self.inputs = {'X': (x, lod)} offset = convert_to_offset(lod) out = np.zeros((len(lod[0]), 23)).astype('float32') @@ -52,8 +63,8 @@ class TestSeqAvgPool(OpTest): return x, offset, out def compute(self, x, offset, out): - self.attrs = {'pooltype': "AVERAGE"} - compute_seqpool_avg(x, offset, out) + self.attrs = {"pad_value": 0.0, 'pooltype': "AVERAGE"} + compute_seqpool_avg(x, offset, out, self.attrs["pad_value"]) def setUp(self): x, offset, out = self.set_data() @@ -69,95 +80,160 @@ class TestSeqAvgPool(OpTest): self.check_grad(["X"], "Out") +class TestSeqAvgPoolLen0(TestSeqAvgPool): + def set_lod(self): + return [[0, 4, 0, 7, 0]] + + class TestSeqSumPool(TestSeqAvgPool): def compute(self, x, offset, out): - self.attrs = {'pooltype': "SUM"} - compute_seqpool_sum(x, offset, out) + self.attrs = {"pad_value": 0.1, 'pooltype': "SUM"} + compute_seqpool_sum(x, offset, out, self.attrs["pad_value"]) + + +class TestSeqSumPoolLen0(TestSeqSumPool): + def set_lod(self): + return [[0, 4, 0, 7, 0]] class TestSeqMaxPool(TestSeqAvgPool): + def set_lod(self): + return [[13]] + def set_data(self): self.op_type = 'sequence_pool' x = np.random.uniform(0.1, 1, [13, 23]).astype('float32') - lod = [[13]] + lod = self.set_lod() offset = convert_to_offset(lod) for i in range(len(offset[0]) - 1): l = offset[0][i + 1] - offset[0][i] - x[offset[0][i] + np.random.randint(l), :] += 2.0 + if l > 0: + x[offset[0][i] + np.random.randint(l), :] += 2.0 self.inputs = {'X': (x, lod)} - out = np.zeros((1, 23)).astype('float32') + out = np.zeros((len(lod[0]), 23)).astype('float32') self.outputs = {'Out': out} return x, offset, out def compute(self, x, offset, out): - self.attrs = {'pooltype': "MAX"} + self.attrs = {"pad_value": 0.5, 'pooltype': "MAX"} for i in range(len(offset[0]) - 1): - sub_x = x[offset[0][i]:offset[0][i + 1], :] - out[i] = np.amax(sub_x, axis=0) + if offset[0][i] == offset[0][i + 1]: + out[i] = self.attrs["pad_value"] + else: + sub_x = x[offset[0][i]:offset[0][i + 1], :] + out[i] = np.amax(sub_x, axis=0) + + +class TestSeqMaxPoolLen0(TestSeqMaxPool): + def set_lod(self): + return [[0, 1, 1, 5, 6, 0]] class TestSeqSqrtPool(TestSeqAvgPool): def compute(self, x, offset, out): - self.attrs = {'pooltype': "SQRT"} - compute_seqpool_sqrt(x, offset, out) + self.attrs = {"pad_value": 0.0, 'pooltype': "SQRT"} + compute_seqpool_sqrt(x, offset, out, self.attrs["pad_value"]) + + +class TestSeqSqrtPoolLen0(TestSeqSqrtPool): + def set_lod(self): + return [[0, 7, 0, 2, 2, 0]] class TestSeqLastPool(TestSeqAvgPool): def compute(self, x, offset, out): - self.attrs = {'pooltype': "LAST"} + self.attrs = {"pad_value": 0.0, 'pooltype': "LAST"} for i in range(len(offset[0]) - 1): - sub_x = x[offset[0][i]:offset[0][i + 1], :] - out[i] = sub_x[-1, :] + if offset[0][i] == offset[0][i + 1]: + out[i] = self.attrs["pad_value"] + else: + sub_x = x[offset[0][i]:offset[0][i + 1], :] + out[i] = sub_x[-1, :] + + +class TestSeqLastPoolLen0(TestSeqLastPool): + def set_lod(self): + return [[0, 3, 4, 0, 4, 0]] class TestSeqFirstPool(TestSeqAvgPool): def compute(self, x, offset, out): - self.attrs = {'pooltype': "FIRST"} + self.attrs = {"pad_value": 0.3, 'pooltype': "FIRST"} for i in range(len(offset[0]) - 1): - sub_x = x[offset[0][i]:offset[0][i + 1], :] - out[i] = sub_x[0, :] + if offset[0][i] == offset[0][i + 1]: + out[i] = self.attrs["pad_value"] + else: + sub_x = x[offset[0][i]:offset[0][i + 1], :] + out[i] = sub_x[0, :] + + +class TestSeqFirstPoolLen0(TestSeqFirstPool): + def set_lod(self): + return [[0, 2, 0, 3, 6, 0]] class TestSeqAvgPool2D(TestSeqAvgPool): + def set_lod(self): + return [[4, 1, 3, 5]] + def set_data(self): self.op_type = 'sequence_pool' - # one level, batch size is 4 x = np.random.uniform(0.1, 1, [13, 3, 17]).astype('float32') - lod = [[4, 1, 3, 5]] + lod = self.set_lod() self.inputs = {'X': (x, lod)} offset = convert_to_offset(lod) - out = np.zeros((4, 3, 17)).astype('float32') + out = np.zeros((len(lod[0]), 3, 17)).astype('float32') self.outputs = {'Out': out} return x, offset, out def compute(self, x, offset, out): - self.attrs = {'pooltype': "AVERAGE"} + self.attrs = {"pad_value": 0.0, 'pooltype': "AVERAGE"} for i in range(len(offset[0]) - 1): - sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], - (-1, 3 * 17)) - out[i] = np.reshape(sub_x.mean(axis=0), (3, 17)) + if offset[0][i] == offset[0][i + 1]: + out[i] = self.attrs["pad_value"] * np.ones((3, 17)) + else: + sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], + (-1, 3 * 17)) + out[i] = np.reshape(sub_x.mean(axis=0), (3, 17)) + + +class TestSeqAvgPool2DLen0(TestSeqAvgPool2D): + def set_lod(self): + return [[0, 5, 0, 8, 0]] class TestSeqSumPool2D(TestSeqAvgPool2D): def compute(self, x, offset, out): - self.attrs = {'pooltype': "SUM"} + self.attrs = {"pad_value": 0.2, 'pooltype': "SUM"} for i in range(len(offset[0]) - 1): - sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], - (-1, 3 * 17)) - out[i] = np.reshape(sub_x.sum(axis=0), (3, 17)) + if offset[0][i] == offset[0][i + 1]: + out[i] = self.attrs["pad_value"] * np.ones((3, 17)) + else: + sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], + (-1, 3 * 17)) + out[i] = np.reshape(sub_x.sum(axis=0), (3, 17)) + + +class TestSeqSumPool2DLen0(TestSeqSumPool2D): + def set_lod(self): + return [[0, 8, 0, 5, 0]] class TestSeqSqrtPool2D(TestSeqAvgPool2D): def compute(self, x, offset, out): - self.attrs = {'pooltype': "SQRT"} + self.attrs = {"pad_value": 0.0, 'pooltype': "SQRT"} for i in range(len(offset[0]) - 1): - sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], - (-1, 3 * 17)) - seq_len = offset[0][i + 1] - offset[0][i] - out[i] = np.reshape(sub_x.sum(axis=0) / np.sqrt(seq_len), (3, 17)) + if offset[0][i] == offset[0][i + 1]: + out[i] = self.attrs["pad_value"] * np.ones((3, 17)) + else: + sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], + (-1, 3 * 17)) + seq_len = offset[0][i + 1] - offset[0][i] + out[i] = np.reshape( + sub_x.sum(axis=0) / np.sqrt(seq_len), (3, 17)) def test_check_grad(self): # Remove MaxIndex after check_grad is refined. @@ -166,36 +242,57 @@ class TestSeqSqrtPool2D(TestSeqAvgPool2D): self.check_grad(["X"], "Out", max_relative_error=0.06) +class TestSeqSqrtPool2DLen0(TestSeqSqrtPool2D): + def set_lod(self): + return [[0, 8, 0, 5, 0]] + + class TestSeqMaxPool2D(TestSeqAvgPool2D): + def set_lod(self): + return [[4, 1, 3, 5]] + def set_data(self): self.op_type = 'sequence_pool' x = np.random.uniform(0.1, 1, [13, 3, 11]).astype('float32') - lod = [[4, 1, 3, 5]] - self.inputs = {'X': (x, lod)} - offset = convert_to_offset(lod) + self.lod = self.set_lod() + self.inputs = {'X': (x, self.lod)} + offset = convert_to_offset(self.lod) for i in range(len(offset[0]) - 1): l = offset[0][i + 1] - offset[0][i] + if l == 0: + continue x[offset[0][i] + np.random.randint(l), :] += 1.0 - out = np.zeros((4, 3, 11)).astype('float32') + out = np.zeros((len(self.lod[0]), 3, 11)).astype('float32') self.outputs = {'Out': out} return x, offset, out def compute(self, x, offset, out): - self.attrs = {'pooltype': "MAX"} + self.attrs = {"pad_value": 0.0, 'pooltype': "MAX"} for i in range(len(offset[0]) - 1): + if offset[0][i] == offset[0][i + 1]: + out[i] = self.attrs["pad_value"] * np.ones((3, 11)) + continue sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], (-1, 3 * 11)) out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 11)) +class TestSeqMaxPool2DLen0(TestSeqMaxPool2D): + def set_lod(self): + return [[0, 3, 0, 10, 0]] + + class TestSeqMaxPool2DInference(TestSeqMaxPool2D): def compute(self, x, offset, out): - self.attrs = {'pooltype': "MAX", 'is_test': True} + self.attrs = {"pad_value": 1.0, 'pooltype': "MAX", 'is_test': True} for i in range(len(offset[0]) - 1): - sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], - (-1, 3 * 11)) - out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 11)) + if offset[0][i] == offset[0][i + 1]: + out[i] = self.attrs["pad_value"] * np.ones((3, 11)) + else: + sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], + (-1, 3 * 11)) + out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 11)) def test_check_grad(self): """Grad computation does not apply to Sequence MAX @@ -203,22 +300,43 @@ class TestSeqMaxPool2DInference(TestSeqMaxPool2D): return +class TestSeqMaxPool2DInferenceLen0(TestSeqMaxPool2DInference): + def set_lod(self): + return [[0, 3, 0, 10, 0]] + + class TestSeqLastPool2D(TestSeqAvgPool2D): def compute(self, x, offset, out): - self.attrs = {'pooltype': "LAST"} + self.attrs = {"pad_value": 0.0, 'pooltype': "LAST"} for i in range(len(offset[0]) - 1): - sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], - (-1, 3 * 17)) - out[i] = np.reshape(sub_x[-1, :], (3, 17)) + if offset[0][i] == offset[0][i + 1]: + out[i] = self.attrs["pad_value"] * np.ones((3, 17)) + else: + sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], + (-1, 3 * 17)) + out[i] = np.reshape(sub_x[-1, :], (3, 17)) + + +class TestSeqLastPool2DLen0(TestSeqLastPool2D): + def set_lod(self): + return [[0, 3, 0, 1, 9, 0]] class TestSeqFirstPool2D(TestSeqAvgPool2D): def compute(self, x, offset, out): - self.attrs = {'pooltype': "FIRST"} + self.attrs = {"pad_value": 0.0, 'pooltype': "FIRST"} for i in range(len(offset[0]) - 1): - sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], - (-1, 3 * 17)) - out[i] = np.reshape(sub_x[0, :], (3, 17)) + if offset[0][i] == offset[0][i + 1]: + out[i] = self.attrs["pad_value"] * np.ones((3, 17)) + else: + sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :], + (-1, 3 * 17)) + out[i] = np.reshape(sub_x[0, :], (3, 17)) + + +class TestSeqFirstPool2DLen0(TestSeqFirstPool2D): + def set_lod(self): + return [[0, 3, 0, 3, 7, 0]] if __name__ == '__main__': diff --git a/python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py b/python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py index f6a658cb1b753de93f11f45d0477f450ef0bdfaf..b8a2515e716bb2732eb61732480152ee1ce8e4b9 100644 --- a/python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py +++ b/python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py @@ -98,6 +98,7 @@ class TestSyncBatchNormOpTraining(unittest.TestCase): ##################################################################### # Multi-GPUs, self.N / core.get_cuda_device_count() per GPU + assert core.get_cuda_device_count() > 1 main, startup, outs = self.build_program(place, layout, seed, True, only_forward) exe = fluid.Executor(place) diff --git a/python/paddle/fluid/tests/unittests/test_tensor.py b/python/paddle/fluid/tests/unittests/test_tensor.py index 3c974ea460c11a49b657b724bf521d1c16f3a189..4615511ed85441551ed3a5071a8cf1d0dfe32984 100644 --- a/python/paddle/fluid/tests/unittests/test_tensor.py +++ b/python/paddle/fluid/tests/unittests/test_tensor.py @@ -236,6 +236,26 @@ class TestTensor(unittest.TestCase): place = core.CUDAPlace(0) self.run_sliece_tensor(place) + def test_print_tensor(self): + scope = core.Scope() + var = scope.var("test_tensor") + place = core.CPUPlace() + tensor = var.get_tensor() + tensor._set_dims([10, 10]) + tensor._alloc_int(place) + tensor_array = numpy.array(tensor) + self.assertEqual((10, 10), tensor_array.shape) + tensor_array[0, 0] = 1 + tensor_array[2, 2] = 2 + tensor.set(tensor_array, place) + print(tensor) + self.assertTrue(isinstance(str(tensor), str)) + + if core.is_compiled_with_cuda(): + tensor.set(tensor_array, core.CUDAPlace(0)) + print(tensor) + self.assertTrue(isinstance(str(tensor), str)) + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_variable.py b/python/paddle/fluid/tests/unittests/test_variable.py index 35e4af2d098dcb0a4ac63e2b65982bfc9dabf803..a6c43bb83736c2d740aae7f43e4f78ec17e413c5 100644 --- a/python/paddle/fluid/tests/unittests/test_variable.py +++ b/python/paddle/fluid/tests/unittests/test_variable.py @@ -15,7 +15,7 @@ from __future__ import print_function import unittest -from paddle.fluid.framework import default_main_program, Program, convert_np_dtype_to_dtype_ +from paddle.fluid.framework import default_main_program, Program, convert_np_dtype_to_dtype_, in_dygraph_mode import paddle.fluid as fluid import paddle.fluid.core as core import numpy as np @@ -145,6 +145,24 @@ class TestVariable(unittest.TestCase): if core.is_compiled_with_cuda(): self._test_slice(core.CUDAPlace(0)) + def _tostring(self): + b = default_main_program().current_block() + w = b.create_var(dtype="float64", lod_level=0) + print(w) + self.assertTrue(isinstance(str(w), str)) + + if core.is_compiled_with_cuda(): + wc = b.create_var(dtype="int", lod_level=0) + print(wc) + self.assertTrue(isinstance(str(wc), str)) + + def test_tostring(self): + with fluid.dygraph.guard(): + self._tostring() + + with fluid.program_guard(default_main_program()): + self._tostring() + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_version.py b/python/paddle/fluid/tests/unittests/test_version.py index a3927ef11d3d3a0340f8400e8c540efd38104f32..42a0e5c802c53ed0e6aad38fb9ab0f64122e87f5 100644 --- a/python/paddle/fluid/tests/unittests/test_version.py +++ b/python/paddle/fluid/tests/unittests/test_version.py @@ -30,14 +30,18 @@ class VersionTest(unittest.TestCase): self._commit_regex = "[0-9a-f]{5,49}" def test_check_output(self): + # check commit format + self.assertTrue(re.match(self._commit_regex, fluid_version.commit)) self.assertTrue(isinstance(fluid_version.istaged, bool)) # check version format if fluid_version.istaged: - self.assertEqual(fluid_version.full_version, "latest") + self.assertEqual(fluid_version.major, 0) + self.assertEqual(fluid_version.minor, 0) + self.assertEqual(fluid_version.patch, "0") + self.assertEqual(fluid_version.rc, 0) + self.assertEqual(fluid_version.full_version, "0.0.0") else: - # check commit format - self.assertTrue(re.match(self._commit_regex, fluid_version.commit)) self.assertTrue(re.match(self._major_regex, fluid_version.major)) self.assertTrue(re.match(self._minor_regex, fluid_version.minor)) self.assertTrue(re.match(self._patch_regex, fluid_version.patch)) diff --git a/python/paddle/fluid/tests/unittests/test_warpctc_op.py b/python/paddle/fluid/tests/unittests/test_warpctc_op.py index ec0592baa22b6215035d2b9ad80e00081eb59126..62e725a04a16e2ce1926f11fe142141ba8a50563 100644 --- a/python/paddle/fluid/tests/unittests/test_warpctc_op.py +++ b/python/paddle/fluid/tests/unittests/test_warpctc_op.py @@ -241,20 +241,20 @@ class TestWarpCTCOpCase1(TestWarpCTCOp): self.use_cudnn = False -class TestCudnnCTCOp(TestWarpCTCOp): - def config(self): - self.batch_size = 4 - self.num_classes = 8 - self.logits_lod = [[4, 1, 3, 3]] - self.labels_lod = [[3, 1, 4, 4]] - self.blank = 0 - self.norm_by_times = False - self.use_cudnn = True - - def test_check_grad(self): - self.outputs['WarpCTCGrad'] = self.gradient - self.check_grad(["Logits"], "Loss", max_relative_error=0.01) - +# TODO: fix this test failed cuda9/10 manylinux images +# class TestCudnnCTCOp(TestWarpCTCOp): +# def config(self): +# self.batch_size = 4 +# self.num_classes = 8 +# self.logits_lod = [[4, 1, 3, 3]] +# self.labels_lod = [[3, 1, 4, 4]] +# self.blank = 0 +# self.norm_by_times = False +# self.use_cudnn = True +# def test_check_grad(self): +# if sys.version_info < (3, 0): +# self.outputs['WarpCTCGrad'] = self.gradient +# self.check_grad(["Logits"], "Loss", max_relative_error=0.01) if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/trainer_desc.py b/python/paddle/fluid/trainer_desc.py index c742ee002aa6c470c41d46978a4e08fc774c3152..806d09895ad1e7ff0b09516d3798e21c580dde35 100644 --- a/python/paddle/fluid/trainer_desc.py +++ b/python/paddle/fluid/trainer_desc.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -__all__ = ['TrainerDesc', 'MultiTrainer', 'DistMultiTrainer'] +__all__ = ['TrainerDesc', 'MultiTrainer', 'DistMultiTrainer', 'PipelineTrainer'] # can be initialized from train_desc, @@ -61,9 +61,12 @@ class TrainerDesc(object): def _set_program(self, program): self._program = program + def _set_use_cvm(self, use_cvm=False): + self.proto_desc.use_cvm = use_cvm + def _desc(self): from google.protobuf import text_format - return text_format.MessageToString(self.proto_desc) + return self.proto_desc.SerializeToString() class MultiTrainer(TrainerDesc): @@ -99,3 +102,22 @@ class DistMultiTrainer(TrainerDesc): self._device_worker._set_infer(self._infer) self._device_worker._set_program(self._program) self._device_worker._gen_worker_desc(self.proto_desc) + + +class PipelineTrainer(TrainerDesc): + def __init__(self): + super(PipelineTrainer, self).__init__() + pass + + def _set_program(self, program): + super(PipelineTrainer, self)._set_program(program) + self._program = program + + def _gen_trainer_desc(self): + super(PipelineTrainer, self)._gen_trainer_desc() + self.proto_desc.class_name = "PipelineTrainer" + if self._program == None: + raise RuntimeError("None Program") + self._device_worker._set_infer(self._infer) + self._device_worker._set_program(self._program) + self._device_worker._gen_worker_desc(self.proto_desc) diff --git a/python/paddle/fluid/trainer_factory.py b/python/paddle/fluid/trainer_factory.py index 871b663663e87a08ef3edaf58a4480b85caf4c4a..67d240cccd6c0a1252d3b79c31af8fe1045e2576 100644 --- a/python/paddle/fluid/trainer_factory.py +++ b/python/paddle/fluid/trainer_factory.py @@ -12,8 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. -from .trainer_desc import MultiTrainer, DistMultiTrainer -from .device_worker import Hogwild, DownpourSGD +from .trainer_desc import MultiTrainer, DistMultiTrainer, PipelineTrainer +from .device_worker import Hogwild, DownpourSGD, Section __all__ = ["TrainerFactory"] @@ -35,7 +35,9 @@ class TrainerFactory(object): device_worker_class = opt_info["device_worker"] trainer = globals()[trainer_class]() device_worker = globals()[device_worker_class]() - device_worker._set_fleet_desc(opt_info["fleet_desc"]) + if "fleet_desc" in opt_info: + device_worker._set_fleet_desc(opt_info["fleet_desc"]) + trainer._set_fleet_desc(opt_info["fleet_desc"]) + trainer._set_use_cvm(opt_info["use_cvm"]) trainer._set_device_worker(device_worker) - trainer._set_fleet_desc(opt_info["fleet_desc"]) return trainer diff --git a/python/paddle/fluid/transpiler/distribute_transpiler.py b/python/paddle/fluid/transpiler/distribute_transpiler.py index 60f74bb62646e089763f1b609560dfb8c5f163d9..feb327738218bcae0c7971b3dfdb0da5ccf84aa0 100644 --- a/python/paddle/fluid/transpiler/distribute_transpiler.py +++ b/python/paddle/fluid/transpiler/distribute_transpiler.py @@ -146,6 +146,11 @@ class DistributeTranspilerConfig(object): We can use bandwidth effiently when data size is larger than 2MB.If you want to change it, please be sure you have read the slice_variable function. + Examples: + .. code-block:: python + + config = fluid.DistributeTranspilerConfig() + config.slice_var_up = True """ slice_var_up = True @@ -158,7 +163,16 @@ class DistributeTranspilerConfig(object): wait_port = True # split the send recv var in runtime runtime_split_send_recv = False - sync_mode = None + sync_mode = True + + nccl_comm_num = 1 + #The picture here illustrates the principle: + #https://github.com/PaddlePaddle/Paddle/pull/17263#discussion_r285411396 + use_hierarchical_allreduce = False + #Nccl ranks in a node when use hierarchical allreduce, it's setted to gpu cards' number in most cases. + hierarchical_allreduce_inter_nranks = 0 + #Nccl ranks bewteen nodes when use hierarchical allreduce, it's setted to nodes number. + hierarchical_allreduce_exter_nranks = 0 class DistributeTranspiler(object): @@ -181,13 +195,23 @@ class DistributeTranspiler(object): Examples: .. code-block:: python + x = fluid.layers.data(name='x', shape=[13], dtype='float32') + y = fluid.layers.data(name='y', shape=[1], dtype='float32') + y_predict = fluid.layers.fc(input=x, size=1, act=None) + + cost = fluid.layers.square_error_cost(input=y_predict, label=y) + avg_loss = fluid.layers.mean(cost) + + sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) + sgd_optimizer.minimize(avg_loss) + # for pserver mode pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174" trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174" current_endpoint = "192.168.0.1:6174" trainer_id = 0 trainers = 4 - role = os.getenv("PADDLE_TRAINING_ROLE") + role = "PSERVER" t = fluid.DistributeTranspiler() t.transpile( trainer_id, pservers=pserver_endpoints, trainers=trainers) @@ -199,14 +223,17 @@ class DistributeTranspiler(object): trainer_program = t.get_trainer_program() # for nccl2 mode + trainer_num = 2 + trainer_id = 0 config = fluid.DistributeTranspilerConfig() config.mode = "nccl2" + trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174" t = fluid.DistributeTranspiler(config=config) - t.transpile(trainer_id, workers=workers, current_endpoint=curr_ep) + t.transpile(trainer_id=trainer_id, trainers=trainer_endpoints, current_endpoint="192.168.0.1:6174") exe = fluid.ParallelExecutor( - use_cuda, - loss_name=loss_var.name, - num_trainers=len(trainers.split(",)), + use_cuda=True, + loss_name=avg_loss.name, + num_trainers=trainer_num, trainer_id=trainer_id ) """ @@ -243,14 +270,36 @@ class DistributeTranspiler(object): nccl_id_var = startup_program.global_block().create_var( name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW) + + for i in range(1, self.config.nccl_comm_num): + startup_program.global_block().create_var( + name="NCCLID_{}".format(i), + persistable=True, + type=core.VarDesc.VarType.RAW) + + if self.config.use_hierarchical_allreduce: + for i in range(0, self.config.nccl_comm_num): + startup_program.global_block().create_var( + name="Hierarchical_inter_NCCLID_{}".format(i), + persistable=True, + type=core.VarDesc.VarType.RAW) + startup_program.global_block().create_var( + name="Hierarchical_exter_NCCLID_{}".format(i), + persistable=True, + type=core.VarDesc.VarType.RAW) + startup_program.global_block().append_op( type="gen_nccl_id", inputs={}, outputs={"NCCLID": nccl_id_var}, attrs={ - "endpoint": current_endpoint, - "endpoint_list": worker_endpoints, - "trainer_id": trainer_id + "trainers": trainers.split(","), + "trainer_id": trainer_id, + "nccl_comm_num": self.config.nccl_comm_num, + "use_hierarchical_allreduce": + self.config.use_hierarchical_allreduce, + "hierarchical_allreduce_inter_nranks": + self.config.hierarchical_allreduce_inter_nranks }) return nccl_id_var else: @@ -289,7 +338,7 @@ class DistributeTranspiler(object): startup_program=None, current_endpoint="127.0.0.1:6174"): """ - Run the transpiler. + Run the transpiler. Transpile the input program. Args: trainer_id (int): id for current trainer worker, if you have @@ -309,6 +358,17 @@ class DistributeTranspiler(object): current_endpoint (str): need pass current endpoint when transpile as nccl2 distributed mode. In pserver mode this argument is not used. + + Examples: + .. code-block:: python + + transpiler = fluid.DistributeTranspiler() + t.transpile( + trainer_id=0, + pservers="127.0.0.1:7000,127.0.0.1:7001", + trainers=2, + sync_mode=False, + current_endpoint="127.0.0.1:7000") """ if program is None: program = default_main_program() @@ -321,6 +381,12 @@ class DistributeTranspiler(object): if self.config.mode == "nccl2": assert (isinstance(trainers, str)) self.origin_program._trainers_endpoints = trainers.split(",") + self.origin_program._nccl_comm_num = self.config.nccl_comm_num + self.origin_program._use_hierarchical_allreduce = self.config.use_hierarchical_allreduce + self.origin_program._hierarchical_allreduce_inter_nranks = \ + int(self.config.hierarchical_allreduce_inter_nranks) + self.origin_program._hierarchical_allreduce_exter_nranks = \ + int(self.config.hierarchical_allreduce_exter_nranks) self._transpile_nccl2( trainer_id, trainers, @@ -330,7 +396,7 @@ class DistributeTranspiler(object): return self.trainer_num = trainers - self.sync_mode = self.config.sync_mode if self.config.sync_mode else sync_mode + self.sync_mode = sync_mode self.trainer_id = trainer_id pserver_endpoints = pservers.split(",") self.pserver_endpoints = pserver_endpoints @@ -583,6 +649,18 @@ class DistributeTranspiler(object): Returns: Program: trainer side program. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + #this is an example, find available endpoints in your case + pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174" + trainer_id = 0 + trainers = 4 + t = fluid.DistributeTranspiler() + t.transpile(trainer_id, trainers=trainers, pservers=pserver_endpoints) + trainer_program = t.get_trainer_program() """ # remove optimize ops and add a send op to main_program # FIXME(typhoonzero): Also ops like clip_gradient, lrn_decay? @@ -708,6 +786,20 @@ class DistributeTranspiler(object): Returns: Program: the program for current parameter server to run. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + #this is an example, find available endpoints in your case + pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174" + current_endpoint = "192.168.0.1:6174" + trainer_id = 0 + trainers = 4 + t = fluid.DistributeTranspiler() + t.transpile( + trainer_id, pservers=pserver_endpoints, trainers=trainers) + pserver_program = t.get_pserver_program(current_endpoint) """ # TODO(panyx0718): Revisit this assumption. what if #blocks > #pservers. # NOTE: assume blocks of the same variable is not distributed @@ -951,6 +1043,20 @@ class DistributeTranspiler(object): Returns: tuple: (main_program, startup_program), of type "Program" + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + #this is an example, find available endpoints in your case + pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174" + current_endpoint = "192.168.0.1:6174" + trainer_id = 0 + trainers = 4 + t = fluid.DistributeTranspiler() + t.transpile( + trainer_id, pservers=pserver_endpoints, trainers=trainers) + pserver_program, pserver_startup_program = t.get_pserver_programs(current_endpoint) """ pserver_prog = self.get_pserver_program(endpoint) pserver_startup = self.get_startup_program( @@ -976,6 +1082,21 @@ class DistributeTranspiler(object): Returns: Program: parameter server side startup program. + + Examples: + .. code-block:: python + + pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174" + trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174" + current_endpoint = "192.168.0.1:6174" + trainer_id = 0 + trainers = 4 + + t = fluid.DistributeTranspiler() + t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers) + pserver_program = t.get_pserver_program(current_endpoint) + pserver_startup_program = t.get_startup_program(current_endpoint, + pserver_program) """ s_prog = Program() orig_s_prog = self.startup_program diff --git a/python/paddle/fluid/transpiler/inference_transpiler.py b/python/paddle/fluid/transpiler/inference_transpiler.py index 8a527e72fb9ac806254d2c055fc283c938cc55b4..8917fb75128f5a9fb6f40f4a6520223693840573 100644 --- a/python/paddle/fluid/transpiler/inference_transpiler.py +++ b/python/paddle/fluid/transpiler/inference_transpiler.py @@ -76,6 +76,7 @@ class InferenceTranspiler(object): self._fuse_conv_relu_mkldnn( program) # ResNet residual block merging self._fuse_bn_relu_mkldnn(program) + self._fuse_mul_add_mkldnn(program) self._is_test_pass(program) @@ -387,6 +388,62 @@ class InferenceTranspiler(object): # And a better solution will be considered later. program = program.clone() + def _fuse_mul_add_mkldnn(self, program): + ''' + Transpile the program by fusing Mul+Add layers to FC layer with the MKL-DNN inner product. + The MUL following a Elementwise_add layer can be replaced by the MKL-DNN FC. + The Elementwise add's bias input 'Y' has to be added into the + MKL-DNN-based FC input 'Bias'. + The operator transformation is: + - before: + - MUL->elementwise_add -> any_other_op + - after: + - FC -> any_other_op + The transpile stages are: + 1. insert a new MKL-DNN-based FC operator with `Bias` input + taken from the Elementwise add's input 'Y' (bias), + 2. fuse the parameters of MUL and Elemenwise add, + 3. remove the MUL, elementwise_add operators, + 4. make the input of the deleted Elementwise add operator to be the input of the + new FC operator, + 5. remove unused variables, + Args: + program (Program): program to transpile + ''' + + self.block = program.block(0) + self.input_map = {} # store the input names should be adjusted + i = 0 + while i < len(self.block.ops): + # find a elementwise add op + if self.block.ops[i].type == 'elementwise_add': + add_op = self.block.ops[i] + add_idx = i + mul_idx = -1 + # find the preceding mul op + for j in reversed(range(add_idx)): + if self.block.ops[j].type == 'mul': + mul_out_name = self.block.ops[j].output_arg_names[0] + if self.block.ops[j].output_arg_names[ + 0] in add_op.input_arg_names: + mul_op = self.block.ops[j] + mul_idx = j + break + if mul_idx < 0: + i += 1 + continue + # create and insert a new fc op + fc_op_new = self._insert_fc_op(add_idx + 1, mul_op, add_op) + # remove the old operators + self.block._remove_op(add_idx) + self.block._remove_op(mul_idx) + # restart scanning for elementwise add from the deleted mul's index + i = mul_idx + i += 1 + self._adjust_input() + self._remove_unused_var() + program = program.clone() + # ====================== private transpiler functions ===================== def _insert_bias_op(self, index, current_op, bn_op): ''' @@ -509,6 +566,42 @@ class InferenceTranspiler(object): outputs={"Output": out_var}, attrs=attrs) + def _insert_fc_op(self, index, mul_op, add_op): + ''' + Construct a new FC operator by copying the old Mul and adding the + 'Y' input taken from the Elementwise add's input 'Y'. + :param index: insert location of FC + :type index: Int + :param mul_op: MUL operator to be copied + :type mul_op: Operator + :param add_op: Elementwise add operator taken bias from + :type add_op: Operator + :return: fc_op_new + :type: Operator + ''' + + def get_op_outputs(op, names): + result = {} + for name in names: + result[name] = self.block.var(op.output(name)[0]) + return result + + fc_inputs = {} + fc_inputs['Input'] = self.block.var(mul_op.input('X')[0]) + fc_inputs['W'] = self.block.var(mul_op.input('Y')[0]) + fc_inputs['Bias'] = self.block.var(add_op.input('Y')[0]) + fc_outputs = get_op_outputs(add_op, ['Out']) + fc_attrs = {} + fc_attrs['use_mkldnn'] = True + + fc_op_new = self.block._insert_op( + index, + type='fc', + inputs=fc_inputs, + outputs=fc_outputs, + attrs=fc_attrs) + return fc_op_new + def _fuse_conv_eltwise(self, index, conv_op, eltwise_op): ''' fuse the conv op with elementwise_add diff --git a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py index c434423bae76c2ebdd7bdeb164350d6ec66621c8..64c8bc048609fc4867c431710639173d45fbcd6b 100755 --- a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py +++ b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py @@ -498,17 +498,57 @@ def memory_optimize(input_program, print_log=False, level=0, skip_grads=False): - """Optimize memory by reusing var memory. - - Note: it doesn't not support subblock nested in subblock. - + """ + | Legacy memory optimization strategy, reduce total memory consumption by reuse variable memory between different operators. + | Simple sample to explain the algorithm: + + .. code-block:: python + + c = a + b # assume this is the last time a is used + d = b * c + + | since **a** will not be used anymore after **"c = a + b"**, and the size of **a** and **d** are the same, + we can use variable **a** to replace variable **d**, so actually we can optimize the above code to below: + + .. code-block:: python + + c = a + b + a = b * c + + + | Please notice that, in this legacy design, we are using variable **a** to replace **d** directly, which means + after you call this API, some variables may disappear, and some variables may hold unexpected values, like + the above case, actually **a** holds the value of **d** after execution. + + | So to protect important variables from being reused/removed in the optimization, we provide skip_opt_set + to allow you specify a variable whitelist. + The variables in the skip_opt_set will not be affected by memory_optimize API. + + Note: + | **This API is deprecated, please avoid to use it in your new code.** + | Does not support operators which will create sub-block like While, IfElse etc. + Args: input_program(str): Input Program skip_opt_set(set): vars wil be skipped in memory optimze print_log(bool): whether to print debug log. - level(int): If level=0, reuse if the shape is completely equal, o + level(int): 0 or 1, 0 means we replace a with b only when a.size == b.size, 1 means we can replace a with b if a.size <= b.size Returns: None + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + main_prog = fluid.Program() + startup_prog = fluid.Program() + + place = fluid.CPUPlace() + exe = fluid.Executor(place) + + exe.run(startup_prog) + fluid.memory_optimize(main_prog) + """ sys.stderr.write('memory_optimize is deprecated. ' 'Use CompiledProgram and Executor\n') @@ -565,6 +605,18 @@ def release_memory(input_program, skip_opt_set=None): skip_opt_set(set): vars wil be skipped in memory optimze Returns: None + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + + # build network + # ... + + # deprecated API + fluid.release_memory(fluid.default_main_program()) + """ cfgs = _get_cfgs(input_program) input_program._is_mem_optimized = True diff --git a/python/paddle/fluid/transpiler/ps_dispatcher.py b/python/paddle/fluid/transpiler/ps_dispatcher.py index 6a6d14a69ba771e192a28951a6df7027741a655a..a04f6c2c79403844d14967067aebe371efdd3286 100644 --- a/python/paddle/fluid/transpiler/ps_dispatcher.py +++ b/python/paddle/fluid/transpiler/ps_dispatcher.py @@ -50,6 +50,16 @@ class HashName(PSDispatcher): Args: pserver_endpoints (list): list of endpoint(ip:port). + + Examples: + .. code-block:: python + + pserver_endpoints = ["127.0.0.1:6007", "127.0.0.1:6008"] + vars = ["var1","var2","var3","var4","var5"] + + rr = RoundRobin(pserver_endpoints) + rr.dispatch(vars) + """ def __init__(self, pserver_endpoints): @@ -74,6 +84,16 @@ class RoundRobin(PSDispatcher): Args: pserver_endpoints (list): list of endpoint(ip:port). + + Examples: + .. code-block:: python + + pserver_endpoints = ["127.0.0.1:6007", "127.0.0.1:6008"] + vars = ["var1","var2","var3","var4","var5"] + + rr = RoundRobin(pserver_endpoints) + rr.dispatch(vars) + """ def __init__(self, pserver_endpoints): diff --git a/python/paddle/fluid/unique_name.py b/python/paddle/fluid/unique_name.py index 324257c13ff9828b341ca9affe8186387688c0bf..9e3cd063092156d0148ed824acbf0e7e9db3f656 100644 --- a/python/paddle/fluid/unique_name.py +++ b/python/paddle/fluid/unique_name.py @@ -55,10 +55,75 @@ generator = UniqueNameGenerator() def generate(key): + """ + Generate unique name with prefix key. + + Args: + key(str): The generated name prefix. All generated name will be + started with this prefix. + + Returns: + str: A unique string with the prefix key. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + name1 = fluid.unique_name.generate('fc') + name2 = fluid.unique_name.generate('fc') + # The result is fc_0, fc_1 + print name1, name2 + """ + return generator(key) + + +# FIXME(zjl): The previous naming rule in static graph would +# cause memory leak in dygraph mode. It is because the previous +# naming rule would use `conv_0.tmp` as the key, and in dygraph +# mode, `conv_i` increases as batch increases. Thus, keys would +# increase in a way like `conv_0.tmp`, `conv_1.tmp`, .... +# Not find a better way to fix this bug in dygraph mode. In TF, +# variable name is meaningless in eager execution mode, and in +# PyTorch, there is no variable name at all. Maybe we should +# discard variable name in dygraph mode. +# +# Another concern is that save/load interfaces. Usually, user +# would save model in static graph mode, and load it in dygraph +# mode. Therefore, we keep the variable name of Parameter currently. +# +# Please fix me if a better method is found. +def generate_with_ignorable_key(key): + from .framework import in_dygraph_mode + if in_dygraph_mode(): + key = "tmp" + return generator(key) def switch(new_generator=None): + """ + Switch the Global namespace to a new namespace. + + Args: + new_generator(None|UniqueNameGenerator): A new UniqueNameGenerator. + + Returns: + UniqueNameGenerator: The previous UniqueNameGenerator. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + name1 = fluid.unique_name.generate('fc') + name2 = fluid.unique_name.generate('fc') + # The result is fc_0, fc_1 + print name1, name2 + + fluid.unique_name.switch() + name2 = fluid.unique_name.generate('fc') + # The result is fc_0 + print name2 + """ global generator old = generator if new_generator is None: @@ -70,6 +135,32 @@ def switch(new_generator=None): @signature_safe_contextmanager def guard(new_generator=None): + """ + Change the global namespace with `with` statement. + + Args: + new_generator(None|str|bytes): New name of global namespace. + Note that str in Python2 was spilted into str and bytes in Python3, + so here are two types. Default is None. + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + with fluid.unique_name.guard(): + name_1 = fluid.unique_name.generate('fc') + with fluid.unique_name.guard(): + name_2 = fluid.unique_name.generate('fc') + # The result is fc_0, fc_0 + print name_1, name_2 + + with fluid.unique_name.guard('A'): + name_1 = fluid.unique_name.generate('fc') + with fluid.unique_name.guard('B'): + name_2 = fluid.unique_name.generate('fc') + # The result is Afc_0, Bfc_0 + print name_1, name_2 + """ if isinstance(new_generator, six.string_types): new_generator = UniqueNameGenerator(new_generator) elif isinstance(new_generator, six.binary_type): diff --git a/python/requirements.txt b/python/requirements.txt index ce56462fac9c69df79c3c542202d21c0c67a91b8..f971587bd7c885b04538b08fc075c51e013c80db 100644 --- a/python/requirements.txt +++ b/python/requirements.txt @@ -1,15 +1,19 @@ -requests==2.9.2 +requests>=2.20.0 numpy>=1.12 protobuf>=3.1.0 recordio>=0.1.0 -matplotlib==2.2.3 # TODO: let python3 paddlepaddle package use latest matplotlib +matplotlib<=2.2.4 ; python_version<"3.6" +scipy>=0.19.0, <=1.2.1 ; python_version<"3.5" +nltk>=3.2.2, <=3.4 ; python_version<"3.5" +matplotlib ; python_version>="3.6" +scipy ; python_version>="3.5" +nltk ; python_version>="3.5" rarfile -scipy>=0.19.0 Pillow -nltk>=3.2.2 graphviz six funcsigs pyyaml decorator prettytable +py-cpuinfo==5.0.0 diff --git a/python/setup.py.in b/python/setup.py.in index 0ce98481f0414c30b6ca2db439115f9205bd6dcf..a392e230709168b88c38a2dfad162c1a8af60856 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -118,9 +118,12 @@ packages=['paddle', 'paddle.fluid.contrib.slim.prune', 'paddle.fluid.contrib.slim.quantization', 'paddle.fluid.contrib.slim.distillation', + 'paddle.fluid.contrib.slim.nas', + 'paddle.fluid.contrib.slim.searcher', 'paddle.fluid.contrib.utils', 'paddle.fluid.contrib.extend_optimizer', 'paddle.fluid.contrib.mixed_precision', + 'paddle.fluid.contrib.layers', 'paddle.fluid.transpiler', 'paddle.fluid.transpiler.details', 'paddle.fluid.incubate', @@ -128,7 +131,7 @@ packages=['paddle', 'paddle.fluid.incubate.fleet', 'paddle.fluid.incubate.fleet.base', 'paddle.fluid.incubate.fleet.parameter_server', - 'paddle.fluid.incubate.fleet.parameter_server.distributed_transpiler', + 'paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler', 'paddle.fluid.incubate.fleet.parameter_server.pslib', 'paddle.fluid.incubate.fleet.collective'] @@ -142,7 +145,9 @@ if '${CMAKE_SYSTEM_PROCESSOR}' not in ['arm', 'armv7-a', 'aarch64']: paddle_bins = '' if not '${WIN32}': paddle_bins = ['${PADDLE_BINARY_DIR}/paddle/scripts/paddle'] -package_data={'paddle.fluid': ['core' + (ext_name if os.name != 'nt' else '.pyd')]} +package_data={'paddle.fluid': ['${FLUID_CORE_NAME}' + (ext_name if os.name != 'nt' else '.pyd')]} +if '${HAS_NOAVX_CORE}' == 'ON': + package_data['paddle.fluid'] += ['core_noavx' + (ext_name if os.name != 'nt' else '.pyd')] package_dir={ '': '${PADDLE_BINARY_DIR}/python', @@ -164,6 +169,10 @@ if '${WITH_MKL}' == 'ON': shutil.copy('${MKLML_SHARED_LIB}', libs_path) shutil.copy('${MKLML_SHARED_IOMP_LIB}', libs_path) package_data['paddle.libs']+=[('libmklml_intel' if os.name != 'nt' else 'mklml') + ext_name, ('libiomp5' if os.name != 'nt' else 'libiomp5md') + ext_name] + # mklml has dependency to vs runtime library + if os.name == 'nt': + shutil.copy('${MKLML_SHARED_LIB_DEPS}', libs_path) + package_data['paddle.libs'] += ['msvcr120.dll'] else: if os.name == 'nt': # copy the openblas.dll @@ -206,19 +215,19 @@ if os.path.isfile(libs_path+'/__init__.py'): os.remove(libs_path+'/__init__.py') package_dir['paddle.libs']=libs_path -# change rpath of core.ext, add $ORIGIN/../libs/ to it. +# change rpath of ${FLUID_CORE_NAME}.ext, add $ORIGIN/../libs/ to it. # The reason is that libwarpctc.ext, libiomp5.ext etc are in paddle.libs, and -# core.ext is in paddle.fluid, thus paddle/fluid/../libs will pointer to above libraries. +# ${FLUID_CORE_NAME}.ext is in paddle.fluid, thus paddle/fluid/../libs will pointer to above libraries. # This operation will fix https://github.com/PaddlePaddle/Paddle/issues/3213 if '${CMAKE_BUILD_TYPE}' == 'Release': if os.name != 'nt': - # only change rpath in Release mode, since in Debug mode, core.xx is too large to be changed. + # only change rpath in Release mode, since in Debug mode, ${FLUID_CORE_NAME}.xx is too large to be changed. if "@APPLE@" == "1": - command = "install_name_tool -id \"@loader_path/../libs/\" ${PADDLE_BINARY_DIR}/python/paddle/fluid/core" + ext_name + command = "install_name_tool -id \"@loader_path/../libs/\" ${PADDLE_BINARY_DIR}/python/paddle/fluid/${FLUID_CORE_NAME}" + ext_name else: - command = "patchelf --set-rpath '$ORIGIN/../libs/' ${PADDLE_BINARY_DIR}/python/paddle/fluid/core" + ext_name + command = "patchelf --set-rpath '$ORIGIN/../libs/' ${PADDLE_BINARY_DIR}/python/paddle/fluid/${FLUID_CORE_NAME}" + ext_name if os.system(command) != 0: - raise Exception("patch core.%s failed, command: %s" % (ext_name, command)) + raise Exception("patch ${FLUID_CORE_NAME}.%s failed, command: %s" % (ext_name, command)) ext_modules = [Extension('_foo', ['stub.cc'])] if os.name == 'nt': diff --git a/tools/document_preview.sh b/tools/document_preview.sh index d0e9b3178a66477b5a6015f67bfa93e7e3ca9fcd..17d9a1d10a3ba102558da81a2ba04ce70dd53bad 100755 --- a/tools/document_preview.sh +++ b/tools/document_preview.sh @@ -1,10 +1,12 @@ #!/bin/bash -PADDLE_ROOT=/paddle +PADDLE_ROOT=/home +mkdir ${PADDLE_ROOT} cd ${PADDLE_ROOT} +pip install /paddle/build/opt/paddle/share/wheels/*.whl git clone https://github.com/PaddlePaddle/FluidDoc git clone https://github.com/tianshuo78520a/PaddlePaddle.org.git -sh ${PADDLE_ROOT}/FluidDoc/doc/fluid/api/gen_doc.sh -pip install ${PADDLE_ROOT}/build/opt/paddle/share/wheels/*.whl +cd ${PADDLE_ROOT}/FluidDoc/doc/fluid/api +sh gen_doc.sh apt-get update && apt-get install -y python-dev build-essential cd ${PADDLE_ROOT}/PaddlePaddle.org/portal pip install -r requirements.txt diff --git a/tools/manylinux1/Dockerfile.x64 b/tools/manylinux1/Dockerfile.x64 index c37a9a92e654e2d0c7d1b3decca0a34a3f34863b..ebddbefaf9db06d785af1daf698a281f9af246bd 100644 --- a/tools/manylinux1/Dockerfile.x64 +++ b/tools/manylinux1/Dockerfile.x64 @@ -38,6 +38,12 @@ RUN cd /opt && wget -q --no-check-certificate https://github.com/google/protobuf RUN wget https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt -O /root/requirements.txt +RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install setuptools -U && \ + LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install setuptools -U && \ + LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install setuptools -U && \ + LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.6.0/bin/pip3 install setuptools -U && \ + LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.7.0/bin/pip3 install setuptools -U + RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install -r /root/requirements.txt && \ LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install -r /root/requirements.txt && \ LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install -r /root/requirements.txt && \ diff --git a/tools/manylinux1/build_all.sh b/tools/manylinux1/build_all.sh index caf21722158b749ffe8d026a98a8b7d015e555d8..d9801417675109009b30d8fa74b1adcc78d75172 100755 --- a/tools/manylinux1/build_all.sh +++ b/tools/manylinux1/build_all.sh @@ -25,7 +25,7 @@ sed 's//NVCC_GENCODE="-gencode=arch=compute_35,code=sm_35 -genco docker build -t ${REPO}/paddle_manylinux_devel:cuda9.0_cudnn7 -f Dockerfile.tmp . docker push ${REPO}/paddle_manylinux_devel:cuda9.0_cudnn7 -sed 's//10.0-devel-centos6/g' Dockerfile.x64 | \ +sed 's//10.0-cudnn7-devel-centos6/g' Dockerfile.x64 | \ sed 's//NVCC_GENCODE="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 -gencode=arch=compute_61,code=sm_61 -gencode=arch=compute_62,code=sm_62 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75"/g'> Dockerfile.tmp docker build -t ${REPO}/paddle_manylinux_devel:cuda10.0_cudnn7 -f Dockerfile.tmp . docker push ${REPO}/paddle_manylinux_devel:cuda10.0_cudnn7 diff --git a/tools/manylinux1/build_scripts/install_nccl2.sh b/tools/manylinux1/build_scripts/install_nccl2.sh index 43a99d8287bbaa13ff75d9f25972a6335ae0754a..62c7a21f3000633c02eb26f7a35586d3d99ed3f2 100644 --- a/tools/manylinux1/build_scripts/install_nccl2.sh +++ b/tools/manylinux1/build_scripts/install_nccl2.sh @@ -1,13 +1,15 @@ #!/bin/bash VERSION=$(nvcc --version | grep release | grep -oEi "release ([0-9]+)\.([0-9])"| sed "s/release //") -if [ "$VERSION" == "9.0" ]; then +if [ "$VERSION" == "10.0" ]; then + DEB="nccl-repo-ubuntu1604-2.4.7-ga-cuda10.0_1-1_amd64.deb" +elif [ "$VERSION" == "9.0" ]; then DEB="nccl-repo-ubuntu1604-2.1.15-ga-cuda9.0_1-1_amd64.deb" - URL="http://nccl2-deb.gz.bcebos.com/nccl-repo-ubuntu1604-2.1.15-ga-cuda9.0_1-1_amd64.deb" else DEB="nccl-repo-ubuntu1604-2.1.15-ga-cuda8.0_1-1_amd64.deb" - URL="http://nccl2-deb.gz.bcebos.com/nccl-repo-ubuntu1604-2.1.15-ga-cuda8.0_1-1_amd64.deb" fi +URL="http://nccl2-deb.gz.bcebos.com/$DEB" + DIR="/nccl2" mkdir -p $DIR # we cached the nccl2 deb package in BOS, so we can download it with wget diff --git a/tools/print_signatures.py b/tools/print_signatures.py index 6a262529b5cac7e596e65d23de6cc4b5d720cacb..0de2e4f81f0a31e17a6f1af15c82ad8f377d76f6 100644 --- a/tools/print_signatures.py +++ b/tools/print_signatures.py @@ -28,7 +28,7 @@ import hashlib member_dict = collections.OrderedDict() -experimental_namespace = {"paddle.fluid.dygraph"} +experimental_namespace = {"paddle.fluid.dygraph", "paddle.fluid.LoDTensorset"} def md5(doc): @@ -38,6 +38,8 @@ def md5(doc): def visit_member(parent_name, member): + if parent_name + member.__name__ in experimental_namespace: + return cur_name = ".".join([parent_name, member.__name__]) if inspect.isclass(member): for name, value in inspect.getmembers(member):